Sharma Ramanathan Deva Devesa

	In accordance with recommendations from the FDA, there are four key actions that biopharmaceutical sponsors can take immediately to offset disruptions:

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 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, clinical research organizations and sites on conducting trial oversight during the pandemic. 
		 

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. Virtualizingtrials 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.
		 

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. To plan for the short- and long-term scenarios, 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 the 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 ZS’s Princeton office. 

 is an associate consultant in ZS’s San Francisco office. 

	Editor’s Note: The full version of this article will be available in our April digital edition on April 13. 

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

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

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Biopharmaceutical sponsors should make urgent decisions now to plan for continued clinical research in the coming months.

4 Key Actions ClinOps Can Take Now to Avoid Disruptions

	On March 14, outgoing FDA commissioner Scott Gottlieb chided pharmaceutical companies and clinical research organizations (CROs) for being slow to embrace innovations in clinical research, even when the FDA sanctions those innovations. “The business model adopted by the clinical trial establishment just isn’t compatible with the kind of positive but disruptive changes that certain innovations can enable,” he wrote 

	The data back him up. Use of innovative designs such as 

 for Phase II and III trials. These innovations can increase the likelihood of launch for new drugs 

	Why, after 15 years, is pharma still struggling to adopt innovative trial designs? There remain many obstacles, including:

		  Lack of sufficient investigator, site, and patient input

		  Poor operational standardization of data and protocols

 of pharma’s approach to innovation among payers and patients 

	FDA keeps churning out guidelines for innovation 

	To foster innovation, the FDA recently offered a slew of initiatives. The agency’s 

 (September 2018) allows for the evaluation of multiple investigational drugs, disease types, or patient populations under a single clinical trial structure. (This is not new: the first “basket” trial, conducted by Novartis, tested imatinib mesylate in five different types of cancer in 

 (August 2018) promotes the use of seamless trial designs, Bayesian models and other advanced statistical approaches. And Gottlieb’s March 2019 press statement was a rollout for the FDA’s new guidelines on clinical trial 

-both practices key to precision medicine.

	However, guidance documents are not the whole story. For example, the FDA’s Master Protocol guidance suggests that sponsors use independent data monitoring committees (IDMCs) to conduct pre-specified and ad hoc assessments of endpoints and recommend protocol modifications. However, 

 has shown that there no standards for IDMC operating procedures, qualifications, and training.

	How can pharma address obstacles to innovation?

	Despite operational obstacles and low adoption rates, the industry has chalked up a few successes with innovative approaches. Effective strategies strike a balance between adopting innovation and mitigating risk. Companies can: 

		 Adopt clinical trial data standards and agreements proposed by organizations such as 

		 Provide patients with a virtual simulation of the clinical trial experience and offer logistics support to facilitate access to clinical trial sites

 that combine on- and off-site data to boost patient compliance and engagement in the process

, especially if the development plan involves using novel biomarkers, non-traditional clinical endpoints, or other innovations

	For its part, the FDA can continue to support innovation by sharing concrete examples of how sponsors and industry have worked together to solve problems and striving for better cross-divisional collaboration, standardization, and communication on crucial drug development issues. 

Venkat Sethuraman, Sharma Ramanathan Deva Devesa, 

FDA commissioner Scott Gottlieb criticized pharmaceutical companies and clinical research organizations for being slow to adapt innovations in clinical research, claiming that the business model adopted just isn’t compatible with the kind of changes that certain innovations can enable.

How Pharma Can Work with the FDA to Embrace Innovation and Overcome Its “Adoptaphobia”

	With nearly 80% of clinical trials failing to meet enrollment timelines, a better, more efficient approach to trial feasibility is critical. Fortunately, the availability of new analytics tools and an increase in data sources has led many companies to adopt a data-driven approach to decision-making during the feasibility process. Yet while this approach can have a massive impact on a trial’s success, oftentimes it’s not enough: Many companies’ efforts to utilize data in their decision-making are often manual, fragmented, and implemented inconsistently.

	How can companies develop and implement a data-and-analytics-driven approach to clinical trial feasibility and, ultimately, improve the success of their clinical trials? To help companies make evidence-based, end-to-end feasibility decisions, we’ve identified seven key building blocks for success: 

Understand the true patient and epidemiology landscape. 

Real-world data can be used for a variety of purposes in trying to identify the highest concentrations of patients. Epidemiology data can help companies develop heat maps and create a detailed picture of where eligible patients might be located, and which clinical sites and physicians are treating them. Real-world data can also be used to simulate medical events and symptoms to identify patients’ rare diseases. Social media data can be used to employ automated social listening techniques, supplementing RWD to identify eligible patients. You can use RWD to identify undiagnosed or underdiagnosed patients with diseases that physicians often struggle to identify. It can also be used to simulate medical events and symptoms to identify patients for rare diseases, which is an unmet need in the market.

Leverage competitive intelligence and landscape assessments. 

Understanding the competitive landscape is an essential step in the clinical trial feasibility process. A competitive intelligence assessment can help you choose investigators who have capacity or are participating in complementary rather than competitive trials. A competitive landscape assessment can help predict key metrics like the number of patients, sites, and investigators available for your trial, and the projected enrollment rates at a country and site level. A thorough understanding of a particular indication will help you create a plan for a trial that takes all other interventions, whether marketed or investigational, and their treatment guidelines into account.

Develop an effective enrollment forecast. 

Enrollment forecasting is an area where companies typically make critical errors. An aspirational enrollment timeline not supported by data can end up significantly impacting trial budgets. Start by looking at the availability of clinical trial patients based on eligibility criteria, and then mapping sites and investigator-to-patient ratios. Then, integrate all of this data to predict enrollment rates. Poisson-gamma and Monte Carlo are two commonly used methods for predicting enrollment curves. Accurate enrollment forecasting can also allow for accurate budget forecasting. Being able to complete these activities with precision can enable more efficient spending and better use of internal resources.

Identifying eligible patients using real-world data can help you determine which countries or regions are best for patient enrollment. Local clinical trial history can be derived either through open data sets, a company’s own internal data, or ideally a combination of the two. Data on average startup times or trial costs can help you decide where to conduct a particular trial based on your timelines and budget.  

Let data drive global site and investigator selection. 

In addition to external benchmarking data and internal data on historical site performance, companies also have data on site quality and investigator experience that can be factored into site selection decisions, such as average query resolution times, protocol deviations, etc. This data can be compared with historical benchmarks to get an understanding of how your company’s metrics compare to competitors.

A comprehensive understanding of the KOL landscape is an essential component of site selection, as having influential physicians spread out across a geography can drive referrals from the community to treatment centers. Beyond traditional KOL mapping techniques, you can utilize artificial intelligence and machine learning to identify influencers based on authorship of papers and practice guidelines, clinical trial participation, and editorial and leadership positions with journals and professional organizations.

Use global drug and asset profiles for your development strategy. 

Analyzing drug development data is important for head-to-head studies to know where comparable drugs are approved, how much they cost, what their approved label information is, and so on. Looking at competitive biosimilars, generics and branded agents’ approval status and market size should help inform a development strategy.

	Current methodologies for assessing trial feasibility won’t carry companies into the future. As our society continues to rely more heavily on data and digital solutions, companies that don’t implement data-driven decision-making will be left behind. On the other hand, clinical programs that leverage data science and analytics during the feasibility process will be positioned to find the right patients at the right sites and in the right regions. 

These seven key building blocks for success are outlined to help companies develop and implement a data-and-analytics-driven approach to clinical trial feasibility.

Sharma Ramanathan Deva Devesa

Articles

4 Key Actions ClinOps Can Take Now to Avoid Disruptions

How Pharma Can Work with the FDA to Embrace Innovation and Overcome Its “Adoptaphobia”

How Data and Analytics Can Improve Clinical Trial Feasibility