Check out the latest features and columns!

Applied Clinical Trials September 2022

Experts discuss solving the EHR-compatibility puzzle in point-of-care trials

‘Record’ Gains in Bringing Research Closer to the Patient

ACT's biennial Salary Survey takes a look at the future of industry following increased adaptation of decentralized technology 

Salary Survey: The Age of COVID

SVP, Head of Clinical Development Operations, Bayer

VP, Head of GCTO Regions and Head Quarters Functions at Merck & Co

The market for contract clinical service delivery models has continued to evolve and grow in step with a maturing global drug development enterprise.Over the course of more than three decades, reliance on outsourcing has intensified, as sponsor companies seek more capacity and scale, scientific and executional expertise, cost containment and operating efficiency. Indeed, spending by biopharmaceutical companies on contract service delivery models is growing rapidly at 6-8% annually, far faster than the rate of spending by sponsor companies on internal R&D operations and infrastructure.

Underlying landscape trends driving growth in demand include the rising prevalence of small companies with highly limited clinical research capacity and talent; leaner sponsor company operating models; and ongoing biopharmaceutical company desire to continuously assess which competencies offer strategic advantages that should be retained in-house or handled by external collaborators.

Twenty years ago, “full-service outsourcing (FSO)” and “Functional Service Provider (FSP)” were distinct models and biopharmaceutical companies tended to gravitate to one or the other.

Over time however, the distinction between FSO and FSP has become harder to define.Today, sponsor companies use a variety of contract service delivery models simultaneously to support their studies, mixing and matching a wide variety of outsourcing approaches that vary from study to study.

In part a result of the variety and combination of approaches used, at this time the terms FSO and FSP are frequently misunderstood not only between companies but also within companies, across functions and therapeutic areas.As a case in point—here are definitions of FSOs in the literature:

“A company hired by another company or research center to take over certain parts of running a clinical trial. The company may design, manage, and monitor the trial, and analyze the results.“

A CRO that “provides planning and execution of a clinical study, including oversight and accountability for key milestone delivery, from our centralized project management team."

“A person or an organization (commercial, academic, or other) contracted by the sponsor to perform one or more of a sponsor's trial-related duties and functions."

‘Full Service,’ for example, has largely become a catch-all phrase that has limited meaning or relevance. Many sponsors characterize their outsourcing approach as Full-Service when, in fact, their approach falls somewhere between FSO and FSP.

There are a number of reasons why there is so much confusion today. Many sponsor companies are simply too large and too fragmented to consistently adhere to a single outsourcing model.Clinical projects have become so complex that they require support from a larger and more diverse group of providers.

 And many large sponsor companies that have formed outsourcing relationships still have significant in-house development resources creating internal competition with comparable external resources.

Legacy activity underway when new partnerships form also contributes to the problem. Clinical teams typically have established their own relationships with select service providers and resist handing-over existing projects to new collaborators. Companies going through mergers and acquisitions, in particular, are often forced to permit inconsistent practices across organizational functions to ensure that legacy projects are completed as new approaches are introduced.

Another reason is the turnover of key senior staff advocating for, overseeing, and implementing a given sourcing strategy. Outsourcing strategies that have not sufficiently been adopted are prone to fall apart when new executives, who might have different ideas about sourcing models, take over.

As a result, in practice, current outsourcing models being used reflect a wide spectrum of permutations and combinations.

Lack of distinction, poor understanding, and misuse of the terms FSO and FSP ultimately fail to provide sponsor companies with meaningful and actionable insight to inform strategic decision-making and continuous improvement. Although biopharmaceutical companies collect a vast amount of operating data today to inform, predict and ultimately optimize clinical trial performance, without a clear understanding of differentiated outsourcing models used, this data offers insufficient insight overall and by function into the contribution and comparative value of a given model or combination of models.

Moreover, although the original definitions of FSP and FSO outsourcing models have not adapted during the past two decades, sponsor company operating models have continued to evolve.As such, it is essential that we update outsourcing terminology to accurately describe the work actually being performed. This can best be achieved through gathering broad industry input and developing consensus definitions that can be easily applied to outsourcing strategies and decision-making.

We propose that the underlying principle now differentiating the permutations and combinations of FSO and FSP-like services is the employment of the CRO’s infrastructure, processes and potentially the CRO’s standard operating procedures (SOPs) for clinical trial conduct.During the past ten years, sponsor companies attempted to differentiate their outsourcing practices according to the type of contracting services (e.g., unit-based, deliverables, outcomes versus FTE) and/or the level of accountability assigned to the CRO. The reality is that sourcing models are adjusted and adapted across multiple service lines as organizations continue to evolve.

Based on extensive input from ICON’s pharmaceutical company Partners of Choice community and in-depth discussions with individual sponsor companies, the following framework (see Table 1 below) has been developed to characterize the variety of sourcing models used across three primary parameters: (1) outsourcing orientation; (2) infrastructure support; and (3) accountability and contracting structure.

Given their size, configuration, portfolio, and core competencies, many of the mid- and large biopharmaceutical companies leverage “mixed models” and combinations of sourcing models at the clinical trial level.Other sponsor companies, for example, have different sourcing approaches depending on the strategic value of the asset in development (e.g., registration/pivot in one model and late-stage/post-approval in another) and the therapeutic area. Table 2 below presents, for illustrative purposes, an application of our proposed sourcing model framework to better characterize the variation in approaches that sponsor companies are using to leverage contracted clinical services.

We invite comments and input into this proposed new taxonomy and framework.It is our hope that this framework will be discussed, refined, and improved.Versions of this framework will ultimately serve as a more relevant and useful approach to identifying models—and combinations of models—associated with higher levels of performance, efficiency, and quality.

With a lack of differentiation between FSO and FSP approaches today, it is critical for both CROs and sponsor companies to align on definitions of sourcing models in use. Universal definitions will ensure common understanding between partners and across the industry with respect to roles, responsibilities, and accountabilities. Consistency in the descriptions we use to characterize “how we work” is also critical for establishing industry-wide recognition and acceptance of benchmarks that enable more meaningful and reasonable comparisons in the effectiveness of one or a combination of sourcing model(s) over others across the life of a development project.All of which will ultimately lead to higher quality work and improved outcomes for patients.

ICON is currently engaging with the Tufts Center for the Study of Drug Development (Tufts CSDD)—with input and in collaboration with ICON’s biopharmaceutical company Partners of Choice (PoC)—to conduct a primary research study comparing clinical trial performance outcomes through the implementation of more meaningfully defined and relevant sourcing model strategies.

, Executive Director and Professor, Tufts Center for the Study of Drug Development, Tufts University School of Medicine

, SVP, Head of Clinical Development Operations, Bayer

, VP, Head of GCTO Regions and Head Quarters Functions at Merck & Co

Kaitin K. Outsourcing outpaces internal spending but remains tactical and reactive

.Impact Report: Analysis and Insight into Critical Drug Development Issues

Brooks K. Contract Pharma’s 20th anniversary retrospective. 

https://www.contractpharma.com/issues/2019-10-01/view_features/contract-pharmas-20th-anniversary-retrospective/

NCI Dictionaries: Dictionary of Cancer Terms [Internet]. C2020. NIH National Cancer Institute. [Cited 2022 Jul 18]. Available from: 

https://www.cancer.gov/publications/dictionaries/cancer-terms/def/contract-research-organization

Full Service CRO [Internet]. C2022. Aptus Clinical. [cited 2022 Jul 18]. Available from: 

https://aptusclinical.com/services/full-service-cro-solution/

Good Clinical Practice Network [Internet]. Avistar Business Solutions. [cited 2022 Jul 18]. Available from: 

https://ichgcp.net/1glossary#:~:text=1.20%20Contract%20Research%20Organization%20(CRO,trial%2Drelated%20duties%20and%20functions

Getz K, Smith Z, Kravet M.Protocol design and performance benchmarks by phase and by oncology and rare disease subgroups.

Articles

Differentiating FSO and FSP approaches can better align definitions of models for CROs and sponsors. 

Redefining CRO Sourcing Model Terminology to Optimize Outsourcing Strategies

Senior Director, Clinical Strategy, Veeva Systems

Sponsors and CROs can improve the quality of clinical trial data and increase efficiency with risk-based quality management (RBQM). Regulatory agencies encourage its use, but only a small percentage of companies have truly adopted a full RBQM strategy. A recent Association of Clinical Research Organization (ACRO) study found that although the percentages of trials incorporating reduced source data verification (SDV) and reduced source data review (SDR) increased from 2019 to 2020, the numbers are low considering the large percentage of trials implementing at least one RBQM component.

Fewer trials relied on SDR and SDV from 2019 to 2020, but not at a rate that shows risk-based quality management is being applied broadly in the industry. 

A likely reason for this could be that it takes nearly six years for innovations supporting clinical execution to go from planning to broad adoption.

 Without an established RBQM strategy, many continue to rely on 100% SDV to ensure compliance. Even with the progress made in applying risk-based approaches, seven out of ten new job postings for clinical research associates (CRAs) still require travel, stating between a 65 to 80% travel requirement.

 This is surprising since SDV is one of the most time and cost-consuming areas in clinical trials, making up 25 to 30% of the total cost of a study.

COVID-19 made monitoring even more challenging since companies hadn’t widely applied RBQM (only 22% of trials included at least one RBQM component),1 and most didn’t have mitigation plans for a global pandemic. Study teams had to refocus their limited resources on higher-risk areas to continue their clinical trials, highlighting the importance of conducting study risk assessments.

RBQM can prepare organizations for unforeseen disruptions while simplifying trial processes. Here are three key learnings from companies that have done this successfully and improved their risk-based approach.

Study teams are focused on maintaining compliance during a trial and often return to SDV to prove data accuracy. But even with 100% SDV, there is a 0.27% error rate,

 so the risk of non-compliance still exists. The manual line-by-line checklist mentality is counterintuitive to the core principles of risk-based approaches and minimizes their value. RBQM strategies should be part of the initial study design to optimize trial quality and efficiency.

Creating protocols with RBQM principles in mind can reduce unnecessary complexity and burden. Regulators realize there is a need for change, and the release of ICH E6 (R3) shows they are providing guidance for the industry that enables more flexibility in conducting trials. The guidance states that clinical trial designs and processes (like SDV) should be proportionate to the risks inherent in the trial and the importance of the data being collected.

By lessening the reliance on crossing items off a list and following a risk-based approach, study teams can remain focused on enhancing remote activities while preserving data integrity and study quality. For example, evaluating study risks during the start-up phase enables clinical leaders to implement mitigations. If a threat occurs, companies will be in a position to take immediate action and enable future preventative measures.

Successful RBQM demands a deep understanding of critical data and processes, quality tolerance limits (QTL), and risk assessments and reviews. By identifying risks upfront, organizations can pinpoint the areas that need the most attention and shift the focus to quality, efficiency, and patient safety.

Once the most significant challenge areas are discovered, it’s best to create a working group of cross-functional resources with direct knowledge and valuable insights about the problem areas or desired outcomes.Establishing this strong risk management foundation will help companies learn their baseline risk level. The early planning will allow organizations to determine the riskiest areas and assign key resources to monitor and mitigate them. Technology can be an enabler to more streamlined and seamless RBQM processes.

ICH E6 (R3) encourages the use of technology, so identifying systems that make applying RBQM strategies easier should be a top priority. Sponsors and CROs must consider systems that embed risk assessment capabilities where the work, tracking, and mitigation or remediation actions are done—in a clinical trial management system (CTMS). This delivers complete, closed-loop issue management for greater efficiency and speed.

While COVID-19 increased risk awareness, the actual risk mentality throughout many organizations and teams has remained unchanged. Fortunately, clinical leaders are looking for new ways to implement preemptive measures that can ensure readiness and limit future business and clinical trial disruption.

The shift from 100% SDV to an RBQM strategy where reduced SDV is implemented will take education and preparation. Start with cross-functional buy-in, informing stakeholders on roles and responsibilities. Training sessions and open forums, led by the RBQM cross-functional working group, can help drive alignment and collaboration. The change in approach will also require coordination and cooperation with other functional areas, such as clinical data management, pharmacovigilance, and statistics.

Stakeholders across these areas have domain expertise to help create risk assessment templates, key risk indicators (KRIs), QTLs, monitoring plans, reporting, and other components within a comprehensive RBQM program. Other success factors include a commitment from leadership to drive organizational change, the ability to evaluate processes from a holistic view (not based on the company’s current structure), and baseline metrics.

The time has come for organizations to prioritize implementing a true RBQM framework. Clinical teams must move beyond the checkbox approach to maximize efficiency and productivity.

To get started, evaluate clinical trial technologies that make adopting RBQM easier. Look for tools that embed the risk assessment within a CTMS and provide risk templates for reuse across studies. Basic Excel trackers or point solutions could add complexity and don’t offer the same efficiency gains. As we enter a new era of more connected, digital clinical trials, it is a great time to explore RBQM and its potential to positively impact trial outcomes and patient health.

, senior director, clinical strategy, Veeva Systems

ACRO, Risk‑Based Monitoring in Clinical Trials: Increased Adoption Throughout 2020, 2022

Tufts Center for the Study of Drug Development, 

Ways to Lower Costs of Clinical Trials and How CROs Help

Impact of source data verification on data quality in clinical trials: an empirical post hoc analysis of three phase 3 randomized clinical trials

ICH E6 (R3) Guideline for Good Clinical Practice (GCP)

With risk-based quality management (RBQM), the industry is uniquely positioned to amend Murphy’s Law and propose something new—“Everything that can go wrong, we can work to identify and prevent.”   

Moving Beyond the Checkbox Approach With Risk-Based Quality Management

Cognizant announced that its Shared Investigator Platform (SIP), a life sciences Software as a Service (SaaS) solution that streamlines clinical trials, has surpassed 250,000 users across 100 countries worldwide. This digital platform facilitates research and collaboration by allowing clinical study documents and safety notifications to be exchanged across site and sponsor teams.

SIP SaaS solution has surpassed 250,000 users across 100 countries worldwide.

Cognizant's Shared Investigator Platform Grows to More Than a Quarter Million Users Globally

Dr. Vince Clinical Research (DVCR) announced the launch of its new clinical pharmacology unit and headquarters in Overland Park, Kansas. The custom-built, green-inspired research complex was designed and engineered with investments in infrastructure, employee wellness, and environmental sustainability.

New custom-built, green-designed headquarters is now open in Overland Park, Kansas.

Dr. Vince Clinical Research Opens Clinical Pharmacology Unit

Dr. Shalaan Beg, VP of Oncology for Science 37, discusses his approach to reducing the time to trial activation at an NCI designated cancer center, how to ensure a diverse representation in clinical trials, and his opinion on decentralized and hybrid trials.

Podcasts

The Oncologist: Patient Conversation Must Include Clinical Trials

Clinical trials are evolving rapidly, making it harder to handle the increasing volume of data available today. Real-time access to this data is crucial to bring groundbreaking new therapies to market. We sat down with Raj Indupuri from eClinical Solutions and Douglas Barta from Cerevel Therapeutics to discuss how a digital clinical architecture is ideal for modern researchers and trials.

We know that data plays a major role in clinical trials. How do researchers handle the volume and variety of data sources?

 Today’s clinical research teams work with an incredible amount of data. In 2021, phase III clinical trials 

 than 10 years prior, according to the Tufts Center for the Study of Drug Development (CSDD), now a million data points on average. However, it’s not just the amount of data that’s harder to manage, it’s the increase in different data sources too. In separate research we conducted with Tufts CSDD, 

more than two-thirds of all sponsors reported using or piloting at least four data types in clinical trials

, including non-CRF data, direct data capture, data from devices and apps, medical images, electronic health records (EHRs) and omics data. In 2022, we see trials averaging 10 unique data sources. To handle all this data, life sciences companies are looking for ways to automate digital data flows including data ingestion, mapping and standardization processes and analytics to support end user teams to clean and review data faster while supporting streamlined decision making and faster insights. 

 In my experience, life sciences companies are looking for innovative strategies to manage numerous data streams coming in from multiple trials to handle the volume, variety and velocity of modern clinical trials. We chose to take a digital first approach and want to maximize flexibility and speed in our trials to ensure clinical teams can choose the best possible providers and technologies that meet the needs of participants and scientific objectives. Sprinkling data across numerous sources is not the approach we wanted to take. One data platform that enables near real-time analysis to help make better business decisions rapidly across teams is a strategic and competitive advantage.

When looking to implement this type of technology platform, you had the opportunity to buy one or consider building your own. What was the strategy behind your approach?

: In recent years there has been a major shift for life sciences companies to buy versus build technology, with more software providers and options available. Buying saves time and money and avoids investing in a team focused outside the core competency of new drug development. We chose to buy a data and analytics cloud including data repositories, workbenches, connectors and out of the box analytics and use it as a foundational platform for our trials. Implementation is faster when working with a software company that does this frequently, and there are always new features to utilize. For companies like ours with the opportunity to design a data architecture from scratch, the benefits of buying far outweighed those of building.

Why is a centralized platform approach necessary when designing a modern digital clinical architecture?

 As trial complexity evolves, a comprehensive data and analytics platform gives researchers the control to quickly examine data from all necessary perspectives. Data flow is automated and rapidly turned into information and insights. Data can be arranged, combined, rearranged, and traced from the original source for better governance, transparency and traceability.

These platforms also help teams collaborate more efficiently with one source of truth and set of visualizations that quickly shows outliers and areas of concern. Many teams still rely on cumbersome, manual Excel trackers and line listings to review data and monitor trials. Instead they could be using a comprehensive platform to transform their data into analytics, risk indicators and actions that enable faster decision-making and can speed cycle time and improve overall data quality.

: One of the greatest challenges facing clinical trials today is timely recruitment of participants and incorporating diverse populations in clinical trials. As sponsors optimize their diversity, equity, and inclusion (DEI) strategies, ensuring plan success is partially a data and analytics problem. Clinical teams need real-time visibility to screening and enrollment demographic analytics by geography and cohort, which often come from multiple source systems. Utilizing a central data infrastructure helps teams operationalize their DEI best practices and gain the oversight necessary to measure results. In parallel, trial designs that incorporate Decentralized Clinical Trial (DCT) designs and technology can help improve the patient experience and make participation in the trial more convenient. The ability to easily incorporate DCT data into digital trials along with numerous other data sources for rapid review and cleaning is another advantage of a centralized data and analytics platform. As we look to the future state of digital trials and clinical development, we know there will continue to be new data streams that will enhance researchers’ knowledge of patient experience that we are ready to take advantage of.

How do you see this technology continuing to advance? Any trends or predictions?

 Data is the currency of life sciences and all the trends we discussed are accelerating. Technology is advancing not only the ways we gather information, but how we standardize and process data. Embracing new trends in data automation, organization and analytics will continue to help research teams make breakthroughs more efficiently. The democratization of data has created greater demand for technology in life sciences that transforms data into insights across all areas of the development process. We see incredible growth and opportunities to build artificial intelligence and predictive analytics on top of centralized data platforms that augment end user knowledge and skill sets, speed the pace of research and advance human health.

: At Cerevel, we are constantly asking ourselves how we can do better for patients, researchers, and our teams. We will continue to invest in technology, analytics and platforms that deliver a better experience to trial participants and our researchers.

Raj Indupuri from eClinical Solutions and Douglas Barta from Cerevel Therapeutics discuss how a digital clinical architecture is ideal for modern researchers and trials.

Digital Clinical Architecture for Modern Researchers

Lead, Emerging Technology Strategy – Clinical Design, Delivery & Analytics, Eli Lilly and Company

Arthur Combs, MD is a clinician, serial entrepreneur and thought leader in wearable technology and digital biomarkers. He serves as a consultant to numerous companies especially those bringing new noninvasive or digital technology to market.

As early as 100 years ago (see photograph of Radio News cover, April 1924) the concept of “decentralized care” seemed just around the corner. Several key elements had to evolve over the past century to make this a reality: the sensors (remote patient monitoring), the telecommunication infrastructure (the Internet), the science, and perhaps most importantly, the buy-in from the medical and patient communities. The COVID-19 pandemic greatly accelerated the latter—to the point where we need to ask: what’s next? What did we learn, and how do we pragmatically move forward?

Depending on how we define them, decentralized clinical trials (DCT) are not new– the concept and early trials with a decentralized design significantly antedated COVID 19. Craig Lipset, clinical research pioneer, cites an internet feasibility study from 2003 and a Boston University patent for “Trials over the Internet” in 2007. But they still are not a singularly defined entity which has led to some confusion around the nomenclature (virtual? hybrid? combinations? site-less?) and execution.

Recently, the Clinical Trials Transformation Initiative (CTTI) 

 defined DCT as trials “in which some or all study assessments or visits are conducted at locations other than the investigator site.” Here, we specifically focus on a 

 of this broad spectrum: how technology can enable participation, leading to lower participant burden and better data. We believe this aligns well with the efforts to provide broader access to a more diverse population, which is encouraged by the latest 

In part, pharmaceutical companies and CROs can conduct viable decentralized drug development trials because of the advent of remote patient monitoring technology, wearable sensors, and electronically gathered data such as ePROs, but also as a result of a more patient centered, real-world data focus. How those trials are established, conducted, and validated evolved significantly under the pressure of the COVID-19 pandemic.The question now is what happens next? What have we learned (both pre- and post-COVID) and how can we put it to practical use? How do the benefits to participant burden and cohort diversity balance with the hard endpoints required to establish safety and efficacy for new drugs? What study design elements, enabling technology, and next generation data management are required for all stakeholders including patients, investigators, sponsors, society to benefit? 

Starting with the positives on the patient side, many aspects of clinical trials can be performed remotely—which, when done right, increases convenience, accessibility, and diversity. Medical grade remote patient monitoring (RPM) devices and digital technology can facilitate obtaining high-quality data—but transforming this data into clinical trial endpoints is a high bar. Most importantly, the foundation needs to be established first, which is required for regulatory approvals, and ultimately for the pharma industry’s buy-in.

As anyone who has spent hours in digital “customer support loops” for their wireless carrier can attest, decentralized processes have to be simple, effective, and efficient in order to ensure patient adherence. We have heard from subjects who are motivated but unable to participate in drug trials due to the travel burden. DCT capabilities such as RPM devices can allow them to participate. These medical devices—mostly wearables—must be designed for patient comfort, reusability, and self-service. Depending on the trial, the participant pool could be tech-savvy millennials, or it could be an older demographic who is much less comfortable with technology and/or relies on a caregiver. Trial participants can represent a broad range of demographics as we seek to expand the diversity of participants for various studies.

The pandemic forced some populations to rapidly adapt to various forms of digital healthcare, including telemedicine. This is a positive development, but it is not enough. There is an opportunity to further capitalize on this adoption by driving participation in decentralized trials and expanding to previously underserved populations. To do so means acknowledging issues and developing solutions to ensure the progress attained thus far is not transient and expedites more patient-centric drug development.

Instead of just throwing technology at the problem, both digital healthcare companies and pharmaceutical companies must seek a more deliberate application of wearable and digital technologies. There is a tension between patient burden and convenience, and the ability to gather the hard, statistically significant endpoints for safety and efficacy that are ultimately needed for regulatory approvals. Effective DCT requires being able to remotely monitor patients and obtain precise data that meets both clinical endpoints and those required for FDA approval.

One key advantage that DCT can deliver using technology is the ability to gather data in the “real world” with a cadence driven by biology. This requires rethinking how we have historically defined endpoints and looking toward more 

l outcomes that are more meaningful than the results of a point-in-time lab or physician assessment. Even the definition of a 

is changing as we better understand individual and circadian variations. Clearly not all endpoints can, or should, be measured at home. As examples, diagnostic imaging or biopsies will not be performed in somebody’s living room. But the key is determining 

 endpoints that can be obtained from a patient performing daily activities that correlate with the scan or biopsy results, and that provide a more granular picture of the disease trend.

Successful DCT trial implementation is not necessarily fully virtual. We believe that hybrid approaches that incorporate remote features, convenient locations, and conventional methods are here to stay for the foreseeable future. Sleep is an illustrative case study. It is said that sleep studies in the clinic are an accurate measure of how nobody sleeps. People tend to sleep better in their own homes, so data derived from a trial conducted with remote technology is less of a burden for the subjects, more representative, and can be performed nightly over time which establishes patterns and derives analyzable data. Having said that, sometimes sleep studies in a local sleep lab can bring valuable insights with technology that is not readily deployable at home. As another example, in considering endpoints for an arthritis study, is the change in range of motion 90 days after the onset of treatment as reflective of efficacy as data on how, and how much, a patient is actually moving—in the real world, when not observed by a physician. That can be the difference between a singular, objective finding and assessing drug efficacy by the subject’s practical function. The ability to record continuous data provides a longitudinal view of a patient instead of snapshots. Researchers can evaluate a patient’s progress wherein each subject is his own control instead of comparing patient A with patient B for real-world analysis of the condition and applicable trends. This new, richer, more objective data will only be beneficial if we have the ability to adequately structure and analyze the various information streams to reap the benefits of a decentralized model.

With a decentralized model, a new approach to trial endpoints is necessary. The recently issued draft 

 regarding digital health technology and RPM for clinical investigations specifically states that novel endpoints should be justified, and the method of assessing a patient’s response should be “well-defined and reliable.” That can present a challenge for RPM and the data streams it produces. Those conducting a study want to ensure the endpoints will meet necessary standards for safety and efficacy, and thus for approvals.

Remote patient monitoring technologies are an important tool in the DCT arsenal. They contribute to the proverbial win-win-win—to have the cake, eat it too, and not put on the extra pounds:

lower burden for patients, which will broaden access,

better and more objective data that links clinical research to the real world; and

faster recruitment, better retention, increased patient diversity, and cheaper trials that allow for more attempts to reach the desired goal.

The pandemic provided a glimpse into what is possible when pharma, (med) tech, CROs, and regulators work together with a sense of urgency against a common threat. To successfully put a dent in the dire unmet medical need, this spirit of cooperation must continue. This is the only way to develop trials that are tailored to the situation, adapt to the patients, and fit into their lives, as opposed to the other way around.

, Lead, Emerging Technology Strategy – Clinical Design, Delivery & Analytics, Eli Lilly and Company

, MD is a clinician, serial entrepreneur and thought leader in wearable technology and digital biomarkers. He serves as a consultant to numerous companies especially those bringing new noninvasive or digital technology to market.

What is next for DCTs after acceleration from COVID-19? 

How Remote Patient Monitoring Technology Can Impact Decentralized Clinical Trials

Chief Delivery Officer, Data Services at eClinical Solutions

The industry-wide jump in data complexity, digitization initiatives and volume of data sources, propelled further by the pandemic, require new approaches to clinical data management. A 

 found 75% of life sciences organizations still using SASand Excel to integrate and analyze data. Over 80% of respondents reported data management activities as time consuming and labor intensive. The study also reported a 40% increase in last patient, last visit (LPLV) to database lock cycle times for companies with five or more data sources and concluded that contending with disparate data sources was contributing to longer database lock cycle times.

Since 2019, the trends impacting clinical data have only accelerated, due in part to adoption of decentralized clinical trial (DCT) models that enable increased remote data collection and greater utilization of local labs. Amidst this disruption, digital and analytics-based modern data management is now an imperative. Here are three ways clinical development leaders can approach this data management evolution by aligning people, process, and technology.

Today, few would deny the importance of a data strategy. The current data environment is too complex not to have one. Most clinical data is generated from sources external to electronic data capture (EDC), and trials routinely average 8 or more data sources; many include 15+. The traditional role of the siloed data manager, focused on cleaning and querying listings of EDC data, is in the past. But in its next iteration, data management has even more value to offer. The data management role has evolved continuously, becoming more technically advanced and growing in responsibility and complexity over the last 20 years. We’ve heard it asked, is the data manager becoming a data scientist? I would argue no. Instead, the data manager has evolved into a data steward, the confident leader who owns and guides the modern data strategy.

This new paradigm demands different skills, too. The data manager as data steward applies a breadth of knowledge across clinical development, from data management to quality and regulatory. They collaborate with clinical operations, programming, and biostatistics with enough professional knowledge of each to enable cross-functional empathy, ensuring all data stakeholders can get what they need from the data. This modern data manager looks at the whole picture the data tells across all data sources, connecting it to their knowledge of the data strategy for the trial. They apply their technical skillset to the many different systems used in development to support evolving protocol requirements and ensure that tech aligns to process improvements in support of accelerated timelines.

Empower data management with the right tools

Variety in acquisition technologies creates flexibility to choose what is best for trials and participants. For clinical data infrastructure and analytics, this means interoperability is critical. Centralized data platforms can increase the quality of data deliverables and reduce manual work positively impacting cycle times. Integrated clinical data platforms ingest and organize all sources and structures of data. They must have the ability to serve all their users, not solely the data manager. Data stewards maximize the use of these types of foundational tools that automate end-to-end data flows and enable greater collaboration and faster time to insights.

Optimize processes around a risk-based approach

It’s one thing to have technologies and another to drive their use, adoption, and utilization. For the data steward, upskilling team members and training in technologies is key. It’s important to consider technology as organization-led and supported from a top-down approach. Involve teams across functions in re-engineering processes around the technology. It's not about bringing technology in, but about maximizing its use for the greatest efficiency gains.

Value is recognized when teams adopt and apply these tech-enabled approaches within the context of their data strategy framework. Self-service analytics focus attention where it’s meaningful and create collaboration opportunities among colleagues from a single source of truth. Teams can identify issues, see what has been reviewed and detect trends and outliers in need of discussion. This approach avoids teams waiting until the end of the trial to integrate data risking rework due to missed insights or outliers.

Data is the final product delivered to FDA—the output of research and currency of the life sciences industry. We have reached an inflection point fueled by breakthrough achievements in science, industry disruption, and intent to accelerate research. The data manager is the steward of this most critical asset through this perfect storm. By evolving their skills and adopting tech-enabled processes, they will guide the way—ensuring modern data strategies produce sustainable efficiencies for the future of data management.

, Chief Delivery Officer, Data Services at eClinical Solutions

Accelerating trends in clinical data are forcing changes in strategy.

What the Future State Really Looks Like for Clinical Data Leaders

Jill Wechsler is ACT's Washington Correspondent

An important component of the White House initiative to speed the development of innovative cancer therapies and other critical treatments for patients is to expand modern biotech manufacturing in the US. The effort also aims to reduce US reliance on foreign sources of bioindustrial materials to avoid supply chain disruptions experienced during the pandemic. President Joe Biden unveiled the National Biotechnology and Biomanufacturing Initiative (NBBI) in a speech on Monday to promote his long-sought Cancer Moonshot, while also announcing a new director for the Advanced Research Projects Agency for Health (ARPA-H).

The plan is to spend some $2 billion on the NBBI program, with half of that coming from the Department of Defense (DOD) to help public and private sector entities expand biotech research and manufacturing capabilities, as spelled out at the White House NBBI Summit, held Wednesday.

 Among its multiple initiatives, NBBI calls on manufacturers to use biotechnology to produce new medicines and specialty chemicals, some as alternatives to petrochemical-based production of plastics, fuels, and other materials.

FDA notably will be “spearheading efforts” to advance regulatory science and to “leverage these emerging technologies” to increase medical supply chain resilience and improve patient access to new medical products. Regulatory reform is a goal, and FDA will work with the US Department of Agriculture (USDA) and the Environmental Protection Agency (EPA) to identify areas of “ambiguity” in federal regulations related to biotechnology and to devise plans for reform and for issuing added guidance. A specific provision supports the Bespoke Gene Therapy Consortium at the National Institutes of Health (NIH) in funding up to six new clinical trials focused on rare diseases that utilize streamlined manufacturing frameworks.

In addition, the Department of Health and Human Services (HHS) issued a long list of NBBI projects that will involve spending $40 million to expand biomanufacturing for active pharmaceutical ingredients, antibiotics, and “key starting materials needed to produce essential medications and respond to pandemics,” according to an HHS fact sheet.

 There is support for advanced education programs, pilots for gene therapy manufacturing methods, expanded cell engineering, and platforms and biosafety programs to reduce risks associated with advances in the field. HHS will bolster FDA research programs for advanced manufacturing technologies and for the Advanced Manufacturing Innovation Hub in FDA’s Office of Counterterrorism and Emerging Threats to encourage platform technologies and collaborations in smart manufacturing and closed loop process controls. HHS also cites the development of guidelines under the International Council for Harmonization (ICH) to advance continuous manufacturing of drugs and to enhance safety evaluation of continuous biotech products derived from human or animal cell lines.

A stated objective for the Biden administration is to reduce US reliance on biomedical products from China and other regions. A BioPreferred Program will require federal agencies to purchase US-made biotech products related to medicine, climate change, and food and agriculture, utilizing massive federal data systems to “drive solutions” for these global issues. Such efforts will prevent future disruptions in biopharma supplies, as experienced during the COVID-19 pandemic, when dependence on imported medical products, protective materials, and test components blocked biomedical production and compromised medical services. The executive order also aims to develop a trained, diverse US workforce capable of creating bio-based products and materials.

https://www.whitehouse.gov/briefing-room/presidential-actions/2022/09/12/executive-order-on-advancing-biotechnology-and-biomanufacturing-innovation-for-a-sustainable-safe-and-secure-american-bioeconomy/

https://www.whitehouse.gov/briefing-room/statements-releases/2022/09/14/fact-sheet-the-united-states-announces-new-investments-and-resources-to-advance-president-bidens-national-biotechnology-and-biomanufacturing-initiative/

https://www.hhs.gov/about/news/2022/09/14/fact-sheet-hhs-takes-action-executive-order-launching-national-biotechnology-biomanufacturing-initiative.html

Administration launches national manufacturing initiative, with aim to reduce US reliance on biomedicine from China and other regions.

Advanced Biopharma Manufacturing Key to White House Cancer Moonshot

 joined HeartcoR Solutions as its director of training. Additionally, 

 has joined the organization as director of ECG analysis and quality control.

MMS Holdings Inc. announced the appointment of 

, who will lead the company’s thought leadership team and efforts to engage sponsors and contract research organizations, and 

, who will lead patient-facing strategies.

Elligo Health Research announced Chief Scientific Officer 

 was recently elected Chair of the Vulcan Advisory Council.

Promontory Therapeutics Inc. announced the appointments of 

, as Vice President of Chemistry, Manufacturing and Controls, and 

 as Vice President of Clinical Operations.

 to the newly created role of Chief Operating and Growth Officer and 

, to Chief Medical Officer and Global Head, Oncology Center of Excellence.

. announced that Sarah Cannon Research Institute (SCRI) has joined the Strata Precision Indications for Approved THerapies (Strata PATH) trial.

 announced it has made Inc. Magazine’s Inc. 5000 list.

LaQuinta Jernigan, Chief Operating Officer for 

, has been recognized as one of 100 most inspiring people in life sciences.

’s Vice President, Patient Advocacy and Clinical Research Diversity, Lori Abrams, has been named a Diversity, Equity, and Inclusion Champion.

The latest business and people happenings from the last month, all in one place.

Featured Content

Applied Clinical Trials September 2022

‘Record’ Gains in Bringing Research Closer to the Patient

Salary Survey: The Age of COVID