Michelangelo, who was born in 1475, built models for his sculptures from wax and clay. The Wright Brothers, circa 1906, reportedly built a few models of the first airplane in their bicycle shop. And the Model T, so the story goes, was so named because of the 19 models (A-S) that preceded its launch in 1908.

One could argue that these geniuses created models and prototypes because they saw value in investing that time: Where were the flaws in their design? The sweet spots? How could they make their creation more beautiful, efficient, useful? The use of a model, logic would dictate, could help eliminate or minimize the risk of failure in the end result. In the above examples, the end results speak for themselves.

Of course, designing a Ferrari isn’t the same as designing a monoclonal antibody; the latter will behave differently because of human variables.

Variables aren’t anything to fear, say experts in the field of simulation; certainly they aren’t reason enough for the $1.48 trillion pharma industry

 to forgo simulation of clinical trials, or to patient-test the protocol.

Until the pivotal trials begin, lots of time exists to learn about the drug and how patients will respond to protocol demands, experts say.

Once the trials start, a sponsor is trusting fate. If one of the usually two FDA-required Phase III trials fail, says Mark Lovern, senior vice president, Certara, “you’re basically up a creek without a paddle.”

Of the differing types of simulation companies portrayed in this article—predicting drug performance in people, designing optimal protocols, or inviting patients to play-act roles in clinical trials—all say their businesses are growing and repeat customers are the norm. The case studies are impressive, highlighting money saved and FDA approvals achieved.

At Medidata, Alicia Staley, vice president, patient engagement, reports that its role-playing studio for patients—who often leave trials because of the onerous demands placed on them—has become so popular with sponsors that within two years of operation it is now a revenue producer.

Exploristics, CorEvitas, and Certara all report growth; in some cases, significant growth.

However, while significant growth is reported here, industry-wide adoption may be lacking, specifically because of time and money.

Reasons vary from a potential fear of adding more time to pre-trial investigation; queasiness over leaning too heavily on AI results; unwillingness to change long-existing exploratory and analytical procedures; and problems with communication between internal company silos. These are the reasons, those interviewed say, for why the use of simulations is still the exception, and not the norm.

Mike Rea, founder of IDEA Pharma, says the few R&D professionals he has spoken with about relying on AI-based simulation results are very wary. “I get the strong sense from R&D that they are skeptical about how far technology can drive things at the moment with AI, including simulations,” he says. AI, adds Rea, helps create ideas and run various scenarios, “but may not reliable enough to make a decision.”

While AI may not be an iron-clad decision, simulation can help point a sponsor in the right direction. Controversies abound over the efficiency and productivity of pharma R&D. In a recent analysis of 16 industry members between 2001 and 2020, the group collectively launched 251 new drugs, representing 46% of all Center for Drug Evaluation and Research (CDER)-related FDA approvals within the time period. The average total R&D expenditure per new drug was $6.16 billion.

Despite any gains that pharma might make in terms of extending lives or making those lives more livable, it is the remarkable length of time that trials take and the stunning financial outlay involved in taking a molecule from lab to regulator define pharma’s measure of success, or not. Simulators think 

trial models can help decrease time and money spent with more accurate patient selection, a more honed indication, and subsequent endpoint. “Anything you can do in the planning phase will help,” says Lovern. “You can use a model as a framework.”

“What unites people who are interested in simulations is the feeling that too much of the decision-making in drug development” is made by the medically trained who rely on their gut feelings and past experiences to make major decisions on drug development and subsequent trials, says Sam Miller, head of strategic consulting, Exploristics, a provider of biosimulation software and biostatistics services.

In short, more quantification, less qualification in the decision-making process.

PubMed doesn’t provide extensive help in assessing whether simulation of any part of the clinical trial process is, or isn’t, a keeper. There were no studies that assessed any type of simulation’s overall value. Wrote Krishnaswami et al. in 2020: “The discipline needs to continue to evolve from one‐off case studies to a paradigm of systematic, best practices‐driven approaches to improving decision-making across the [drug discovery, development, regulatory approval, and clinical utilization] continuum.”

In a recent study, researchers simulated the effects of a central nervous inhibitor called HSK3486.

 Designed as an anesthetic, HSK3486 was developed in 2019 to guide model-informed drug development (MIDD) on certain populations like infants and the elderly. HSK3486 was designed to have better anesthetic properties and less systemic exposure than the widely used propofol, which tends to stay in the liver. Using MIDD to test for exposure, the researchers found that predicted systemic exposure went up somewhat in those with hepatic impairment and the elderly, which was similar to the data gathered in a subsequent clinical trial.

FDA supports MIDD; it has done so since 2018. The agency runs what it calls its MIDD-paired meeting program, so sponsors and agency experts can discuss different modeling approaches. A successful modeling application, writes the agency, can raise the odds of a drug’s approval and help determine the right dosages for various populations “in the absence of dedicated trials.”

Certara, says Lovern, has enabled 100 novel drugs, found using physiological based pharmacokinetic modeling and simulation, to obtain label claims in lieu of clinical trials. Most are for drug-drug interactions, he says.

An Exploristics’ case study also shows simulation success. A biotech working on a molecule for a rare disorder came to Exploristics to see the probability of this molecule in a particular indication. It was showing possible efficacy in a few different ones. The biotech was looking for a Phase II trial. It had been told it needed 1,000 patients.

Miller says they ran many assumptions regarding various endpoints and different patient groups to recruit. All those scenarios showed less than 50% of probable success. So it pared down the indication and honed the endpoint to mortality. The result: a new enrollment of 180 patients, and at least 80% success rate. The results showed benefit to patients by enabling a trial that otherwise was unfeasible, says Miller.

Computer power and data retrieval have made simulation possible. Before the 20th century, the technology didn’t exist for researchers to look at bodily processes, AKA pharmacokinetics, to measure the minute amounts of drug present in blood and tissues, says Lovern. It wasn’t until the 1980s that computing power enabled analysis of the large quantities of data needed to inform predictive pharmacological models.

However, even with the technology, data at scale, remains elusive. It’s either proprietary or outrageously expensive to get from a provider. Jane Myles, program director, Decentralized Trials & Research Alliance, says that even within a pharma company itself, the silos don’t share. “Our computational vision is limited to that silo,” she notes.

Exploristics and Certara know about data limitations. Both mostly rely on public sources for data and engage teams of scientists to scour PubMed and the like. Certara uses about 30 data scientists. And Exploristics, says Miller, increases its data pool in a singular way. Its clients agree that the company can access the simulated information to add to future simulations. So far no one has said no, he says. According to Miller, Exploristics’ business has grown at least 30% year over year for some time.

Myles says audience members attending one session at the Drug Information Association (DIA) Annual Meeting in June were asked if they incorporated patient voice into their protocols. Fewer than 40% was the response—a far different percentage than found in the DIA-Tufts Center for the Study of Drug Development patient engagement studies conducted a few years ago. Asked a similar question, 65% of those surveyed said they are “investing in patient-centric initiatives in drug development.”

The following are good examples that our interviewees shared in regard to patient-centric simulations.

Five years ago, Medidata wanted to know how being patient-centered would impact clinical trial design. The company’s so-called design studio was the result. The studio, Staley tells 

, is an “opportunity for [the in-house] team to bring problems into the studio.”

The studio is in operation every month; Medidata has a set group of patient advocates and others they can call for specific disease states. Product managers, designers, and engineers bring their problems to the patient team. And these aren’t all new problems—some have been worked and reworked over the years.

One tool that has received such attention is eConsent. The patient advocates—all of whom have some disability—go through a mock scenario and assess every aspect: ease of setting up an account, language comprehension, even if the screen is presented in an “empathetic way.”

Some patient advocates have left their mark, says Staley. A lung cancer patient advocate that she worked with had been in seven lung cancer trials and had received 60 brain scans. The patient had contrast dye (gadolinium) in her brain, of which there are no clear safety guidelines to determine a maximum exposure to gadolinium, notes Staley. The clinical trial teams had not paid attention to this aspect of the patient’s care plan. A team will not act, says Staley, if information isn’t in front of them. “No one is overseeing all the data points,” she points out.

What patient simulations provide is control, notes Staley. “What we are delivering to the market is a team of patients who have looked at it, taken it apart, and put it back together. That is a cost savings right there, that gets back to industry.”

At CorEvitas, Stephanie Terrey, senior director, patient experience, has a team that gathers recruited patients, study coordinators, the principal investigators, and other physicians. An outreach manager is responsible for patient recruitment. Terrey says the protocols are at the almost finished stage. Once the patients and site staff role play key aspects of the protocol, debrief interviews are conducted with all participants and the feedback is “analyzed and then [CorEvitas] reports to the client,” she explains.

Terrey says there have been times when the patient response has influenced protocol change, such as reducing the number of visits, adding a car service, or easier samples drop-off; even the trial’s design from traditional to decentralized.

In another recent simulation between two principal investigators, Terrey relays that one spoke with empathy, the other went straight down the list of requirements. “We are doing the same protocol, but how patients responded was very different, which is valuable to inform site selection and training,” she says.

A decade ago, at Roche, Myles and colleagues built an “imaginarium” for Roche colleagues who were designing trials. Once a team had assessment schedules and the primary endpoint was established, imaginarium staff looked for real patients. It also brought in important team members—key opinion leaders, clinical scientists, and biostatisticians.

The team also included a graphic artist, for visual depictions done in real time. Myles says, “It was so useful, he would be listening for different things, for example, the emotion in the room.”

But those who were most surprised by what they learned from patients in the two-year patient imaginarium were the biostatisticians and clinicians. “They didn’t know how much they were asking of patients,” says Myles, for example, such as staying in an infusion suite for 12 hours.

Even communication among simulators can be difficult. Lovern says oftentimes, bioengineers and statisticians are speaking a language the decision-makers do not understand. The “modeling nerds” have the information, but they can’t communicate that meaningfully.

“It is like they use different vocabularies,” he says. “The engineers have to explain things in non-tech terminology, or even better, in a therapeutic context. This way, the physician can say, ‘I understand what you’re trying to say and why it’s important to the decision that I am trying to make.’”

With the successes and challenges of simulations, it still comes down to time and money. For industry, time is a business driver. Staley, a three-time cancer survivor, says, for the patient, time is life. Industry must move faster for patients.

 is a freelance writer for medical, clinical trials, and pharma information.

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Articles

Feature Articles

Experts weigh in on the latest advances and adoption trends.

The Pulse of Modeling and Simulation in Clinical Trials

Patients and their caregivers know their unique needs and challenges better than anyone else. Though clinical trial sponsors cannot always anticipate the right path forward, actively listening to patients to understand which (and how) specific protocol elements can hinder or enhance engagement can help shape trial design. A comprehensive “patient empathy mapping” strategy can implement several key methodologies to help sponsors, their contract research organization (CRO) partners, and study teams gauge which patient-friendly elements— whether traditional, hybrid, or decentralized—can or should be considered in trial design and may be beneficial to the current target population.

Uncovering areas of perceived burden and patient preferences creates a strong foundation that can be adjusted and updated during trial design to best meet patient needs and to maintain their interest as valued partners in the process. This can be achieved through a multipronged approach, including activities described ahead, that provides the sponsor with a holistic scope of intelligence to better prioritize which trial design elements patients truly need or want.

Direct surveys: Capturing the patient voice

Hearing directly from patients and caregivers for real-time insights into participation motivators and barriers is critical to successfully preparing for patient-first trial design. Survey groups may be found in proprietary, company-specific patient communities and registries, advocacy groups, online communications organizations that reach rare-disease populations, and similar databases.

Because patient populations vary in therapeutic need, age, geography, race/ethnicity, and socio-economic status, surveying provides a holistic view of what barriers impact which subpopulations more and how to prioritize solutions accordingly.

For example, to learn from patients with cardiovascular disease or related risk factors about their experiences with and opinions of trials, IQVIA surveyed more than 600 screened and qualified patients (primarily 60-years-old and older with cardiovascular disease or risk factors) to determine potential participation deterrents. Trial length, number of required study visits, and mode of medication administration (e.g., oral, injection, etc.) were not priority concerns. Instead, financial motivators (e.g., participation compensation, lack of treatment fee, etc.) were the top-cited factors for deciding to participate. For patient enrollment strategies, this may mean prioritizing how we address financial barriers, such as including the patient stipend and related details in the informed consent form and ensuring that site coordinators can walk participants through financial concerns as part of site education during consent.

When gauging how decentralized a trial may need to be to ensure participation, the survey found that more than half of respondents recognized that virtual trial solutions like telehealth may reduce participation burden, and 82% said they’d be comfortable using a mobile device to participate. However, only 15% of respondents preferred in-home visits by a medical professional over in-person visits for routine study assessments, which require travel to and from sites. So, while we recognize the convenience of these decentralized trial components, it may not be a priority for this specific patient group. Rather, nearly half (49%) of the respondents said they would like travel assistance to and from site visits, which again emphasizes the value placed on financial support.

There are more than a billion unique non-identified patient records currently available. By leveraging an extensive breadth of real-world data (RWD) (e.g., claims data, medical records, etc.), sponsors can gauge how potential patients may view trial procedures based on their current experiences and usual disease care. These insights can show how many patients are familiar with trial procedure and how frequently they may experience it. Comparing that to how many times these procedures may be required within a trial, which could be more frequently, should provide a better understanding of the patient’s level of willingness to participate.

While drafting trial protocols, understanding patients’ willingness to participate based on what they believe is expected of them can improve recruitment and retention. By leveraging patient-centric insights gained through surveys, virtual, or face-to-face focus group discussions, RWD, and more, it is possible to apply analytics to assess patient burden based on trial design aspects that impact willingness to participate in studies or may require more aggressive mitigation.

 Using those analytics, protocol elements can be scored to quantify patient burden. Using burden-scoring for each element, sponsors can examine whether protocols are at the expected level of burden when compared to trial protocols for a similar phase, therapeutic area, disease, etc.

This analysis can be further broken down to provide insights according to race and ethnicity to identify and reach better patient subpopulations. This type of scientific assessment should more reliably predict patient satisfaction and recruitment success.

Analytics and artificial intelligence/machine learning for design

Sponsors have access to unprecedented amounts of detail on patient journeys via multiple data sources, so it is important to understand data source collection approaches and data standardization and curation processes that allow enhanced analyses. Specifically, understanding potential limitations and opportunities with individual data sources is a key first step. For example, claims data do not allow for capturing all meaningful variables and relevant endpoints, such as death dates to calculate overall survival in oncology patients.

When we understand what data sources can and cannot provide, artificial intelligence (AI)/machine learning (ML) approaches can help aggregate data from multiple sources for fine-tuned analysis, resulting in patient-centered insights. AI/ML-driven analysis can help sponsors understand the real-world patient journey, identify subpopulation needs with different or better clinical response to the same therapy, and then adjust eligibility criteria accordingly. Advanced ML methodologies can capture a large group of patients’ voices and analyze this data to create and model potential scenarios to improve clinical responses and/or identify better diagnostic techniques.

One example is the use of the Shapley values technique in ML, which practically applies the Shapley game theory by understanding how each variable or feature contributes in a final outcome.

 This methodology can be used to identify a subgroup of patients with severe asthma who are at higher risk for acute exacerbations as well as to show how patients who may respond differently to the same therapy can potentially benefit differently, too.

 By recognizing these unique patient needs, sponsors can tailor clinical development plans and specific trial designs with the best elements for the target patient population.

Supporting patients through necessary trial elements

Because some trial protocol elements are necessary regardless of how patient-friendly they are or aren’t, sponsors, CROs, and study teams must think through the brunt of the burden for that necessary element and how to best support the patient, site or other stakeholder through the process. In these instances, additional patient education may help to show why this element is critical.

Animated study explainers to view on-site or printed brochures to take home and discuss with their families are key examples of tailored communications to help the patient through what may be an engagement roadblock.

In some cases, protocol adjustments will have to be made during the trial based on ongoing collection of patient insights. For example, a study examining a weight-loss treatment for patients with obesity may require volunteers to capture daily food intake in an electronic diary. However, patients may say they are discouraged by this process. Sponsors will then need to determine whether it’s necessary to secure the data from patients.

If yes, sponsors may adjust the design of the e-diary/decentralized clinical trial technology, or enhance motivating messaging within the data capturing process to better support patients in their journey. This is where gathering patient intelligence on trial procedures and elements prior to trial startup is beneficial.

A listening-first approach to patient outreach

When patient preference insights are available early in trial design, sponsors, CRO partners, and study teams have a tremendous opportunity to “get it right” from the start, including how, when, and where to approach potential patients for participation.

Patient preferences can be reflected in study branding materials, marketing campaigns, and media channels, all the way down to the very last detail on how outreach emails and other messages are written, including the best hour of the day to send them and what patient subpopulation(s) will react to which channels of communication.

Listening to patients prior to outreach provides a chance to gather feedback about the messaging that is used to communicate with patients (e.g., advertising). For a cardiovascular study, if patient insight activities have already informed the sponsor that the number of electrocardiograms will present a challenge for trial interest and retention, tailored patient outreach could ask for feedback or preferences regarding support services that may ease that known burden, such as extended office hours to avoid time off from work or participant stipends to defray childcare costs for longer visits.

Because patient-first trial design will never be a one-size-fits-all approach, it is vital to understand the deep variations in patient populations and their preferences to best cater to their needs, starting with outreach communications. Questions to answer to account for variations, include:

Based on potential participant location, how accessible are stable internet connections? Are they likely to connect via mobile devices, laptops, or other smart devices?

How much do they know about research in general, much less the trial at hand? What are health literacy levels, which indicates the level of educational messaging needed?

Are participants receiving information via culturally preferred messages and channels?

What type of media do patients consume? Do they prefer to read, listen, or both, and how much time do they spend?

Is there a community leader and/or patient association representing patients in this town/state/country who should be engaged first?

Guiding current and future patient-first trial design

Always keeping patients’ needs top of mind and constantly gathering and evaluating insights before, during, and after trials can help inform future pipeline programs and create building blocks to improve patient-centered trials.

Whether it is decentralized trial elements, financial support, or educational information, patients are speaking up about what they need to participate in research that can impact their health and others’. They want to be informed about study progress, results, and related implications. As such, sponsors, investigators (medical practitioners), CROs, and study teams are well-positioned to ask for insights and translate findings into an experience that genuinely works for the patient, ensuring they are engaged in the process, feel valued and motivated to do more, and will become advocates of clinical research.

, patient centricity data manager, experience optimization team, 

, director, patient recruitment strategy, and 

, director, design analytics; all with IQVIA

Cameron, D.; Willoughby, C.; Messer, D.; et al. Assessing Participation Burden in Clinical Trials: Introducing the Patient Friction Coefficient? 

https://pubmed.ncbi.nlm.nih.gov/32741647/

Game Theory. Lecture Notes by Y. Narahari, Department of Computer Science and Automation, Indian Institute of Science, Bangalore, India. October 2012, 

https://gtl.csa.iisc.ac.in/gametheory/ln/web-cp5-shapley.pdf

Walls, C. Identifying and Characterizing Asthma Subgroups at High Risk of Severe and Life-Threatening Exacerbations with Machine Learning and Longitudinal Real-World Data. Pharmaceutical Management Science Association. May 1, 2023, 

https://www.pmsa.org/past-presentations/2023/1793-identifying-and-characterizing-asthma-subgroups

Approaching protocol design and operational planning with patient empathy. 

Executing Trial Design With Patients—Not For Patients

Industry consensus has solidified around the concept of patient-first: the idea that when clinical trials are designed with patient optionality, everyone benefits—from patients, caregivers, onsite staff, and trial sponsors to the entire population that might someday receive the treatments being tested. As a result, patient-centric innovations are being increasingly deployed to solve many of the traditional challenges associated with clinical trial participation and execution. From remote patient onboarding and consent to connected devices and televisits, patient optionality reduces the time and cost burden of in-person site visits, expands participation to previously underserved patient populations, and may even have the potential to accelerate trials.

Patient-first technologies are also embracing patients as experts in their own health. Clinical outcome assessments (COAs)—which encompass patient-reported outcomes (PROs), observer-reported outcomes (ObsROs), clinician-reported outcomes (ClinROs), and performance-related outcomes (PerfROs) that reflect how a patient feels, functions, or survives in a trial—are more personal and easier to access in their electronic form. Specifically, electronic PROs (ePROs) are growing in popularity with sponsors across a wider percentage of their clinical programs.

Advanced ePROs provide rich and accurate data that is both easy for patients to input and easy for sites to share with stakeholders—especially important as regulators are asking

 for more patient self-reported data in trials. However, some hurdles block the path of ePRO’s widespread adoption, including user-friendliness and troubleshooting of the technologies themselves. In the name of patient centricity, the industry must work better together and clear those hurdles to allow the patient voice to be captured.

ePRO isn’t new, but progress has been slow

Required by FDA and the European Medicines Agency (EMA), COAs—again, encompassing PROs, ObsROs, ClinROs, and PerfROs—are part and parcel of clinical trials across the globe.

Because electronic data can be easily tracked and time-stamped, is confidential, and enables easy monitoring, ePROs have become an increasingly attractive option to sponsors for clinical trials—especially decentralized clinical trials (DCTs) or hybrid trials spread across communities. Some large pharmaceutical companies are using ePROs for nearly every trial and want to bring the process of building ePROs in-house for greater control and efficiency. In-house ePRO-build is a relatively new strategy. In the past, most companies considered the risks to be too high for a majority of studies and limited their use of ePRO to collect patient claims and health-related quality of life reports only.

Essentially, ePRO was only mandated when the primary endpoint was supported by patient-reported data. If those data in another trial were not tied to these critical endpoints, then it was collected on paper due to cost and complexity.

But the COVID-19 pandemic changed everything. Overnight, sponsors experienced unprecedented pressure to develop and test treatments—and they had to do it all remotely. The rise of DCTs caused ePROs to be included in more protocol designs and expand their use into therapeutic areas where the primary endpoint was not necessarily derived from patient-reported data. Bringing patient and caregiver reporting in-house is a rising trend now among sponsors and contract research organizations (CROs) because the volume has increased. The share of clinical trials using ePRO is now rapidly expanding, creating more opportunities to engage patients with an app already in their hands.

The following major clinical technology categories dominate the market today:

Randomization and trial supply management (RTSM/interactive response technologies)

Electronic clinical outcome assessments (eCOA)

Out of these five groupings, eCOA is the only one that has not successfully transitioned to an enabled, in-house, configurable SaaS model at scale—although, this is accelerating. Technology companies are releasing new capabilities to make this a reality, and sponsors and CROs are adopting new processes, budgets, resources, and change management strategies to make it possible. The pandemic has hastened this trend and the rise of DCTs has forced new models that will shape a digital transformation across clinical operations. Grandview Research projects the ePRO market to exceed $4 billion by 2030.

The benefits of ePRO (and all eCOAs) extend far beyond a once-in-a-lifetime public health emergency. Compared to onsite paper assessments, ePRO-generated data is easy to share across all stakeholders in the drug development chain. Systems and surveys are easy to adapt, too, based on how the trial is progressing. And when ePROs are directly connected to wearable patient monitoring devices, the speed, reliability, and accuracy of data is even more significant.

Beyond measuring cures, clinical worsening, or mortality, today’s modern ePROs are asking equally important questions about patients’ quality of life. They allow for the implementation of branching the logic of questions—skip patterns, which create a custom path that varies based on a respondent’s answers and offers patients a more personalized experience. Skip-pattern functionality can also shorten the length of questionnaires, reducing patient burden. Finally, ePROs can improve patients’ willingness to answer sensitive questions

 that they otherwise may not be comfortable answering.

The ePRO approach likewise elevates patient voices. For example, one of the five core aspects

 of the patient experience that ePROs should measure, according to FDA, is “role function,” which measures the overall impact of a treatment on a patient’s ability to work and carry on with daily activities. This goes beyond sterile measurements of symptoms and side effects to ask the patient, “How is this going for you in real life?” When a drug’s side effects might outweigh its benefits, real-life impact is incredibly important for researchers.

Finally, ePROs reduce burdens on the patient. Consider a patient in an oncology trial who is suffering from fatigue and all the other effects of their disease. As opposed to requiring that patient to travel to the trial site to report their outcomes, an ePRO allows them to submit their assessment on their own time from home. The patient can probably even report from their own device, which they are already familiar with—which research

 shows most patients prefer. The comfort and flexibility of their ePRO experience means the patient is less likely to drop out of the trial, too, for improved patient retention.

Indeed, recruiting and retaining trial participants is a widely recognized hurdle in trials. Medable spoke to a member of its Patient/Caregiver Network, Katie Moureau, who shared her experience. Moureau’s eight-year-old son has Prader-Willi syndrome and has participated in several hybrid and DCTs where ePROs were used.

“Being in the rare disease community, there’s already a limited number of participants,” said Moureau. “And now throw on top of that, are patients unable to afford to travel or take off work to go the distance of far-away sites? That’s difficult for families. In a DCT, you’re able to do most, if not all, of that in your home. I think that’s going to help us move science forward and give sufferers of rare diseases more potential for their future and less burdens on their families and themselves.”

Beyond increasing patient access to trials, ePROs can also help patients and caregivers remember to report in the first place. “An ePRO app that provides reminders and well-timed prompts is really helpful,” added Moureau.

It’s clear that ePROs pose benefits, but hurdles still exist on the path to adopting them at full-scale.

For example, building ePRO applications takes too long. In a few years, the majority of ePRO-builds will be automated using libraries and templates—perhaps aided by new AI technologies—but changes need to happen now. The patient experience should improve as well as more integrations with existing software platforms become easier and more configurable, which also prevents patients from having to toggle between multiple apps for the same trial. Ultimately, the ideal solution will be one platform that unifies the trial experience, even if the capabilities stem from different applications.

The life sciences industry is learning from the plight of trial site teams who, on average, are required to use 12 to 17 different systems and logins per study. While the industry continues to work to solve this problem at the site level, this cannot happen to patients, as the negative experience could cause extra burden and increase dropouts.

The interoperability of ePRO data also has room for improvement. The Clinical Data Interchange Standards Consortium (CDISC) has already created mature standards for clinical research that are accepted by FDA and global regulatory agencies while HL7 is the accepted standard for healthcare.

The challenge is in bridging research and healthcare data together bidirectionally.

Data interoperability has two sides: sponsors and patients. Standardizing ePRO data—and all eCOA data—will help optimize data validation and conformance, as well as improve data transfer by eliminating the need for data manipulation before it is sent to external stakeholders, which helps the sponsor. Patients, however, still struggle to receive any of their data at the end of a trial. The concept of “patient data return” has only been explored by a few large sponsors, with Pfizer

 being one of the only to succeed. Once the industry reaches a point where patients get their study results returned to them in a standardized way, it can be added to their medical records and potentially provide more opportunities to patients to find medical solutions. Patients are also more likely to share their data to help others.

There are some initiatives in progress that offer a good model to follow. The Vulcan Accelerator,

 for instance, was launched in 2019 by a group of industry stakeholders to bring research and healthcare together through standardized data exchange. The initiative is testing various use cases with results expected over the next five or more years. Ultimately, success will unlock many opportunities for clinical research and patients.

Technical support is another challenge, with patients resorting to calling help desks and clinical trial administrators spending time helping patients troubleshoot. Existing ePRO solutions also require a great deal of training for sites before a trial can start, which is counterproductive to the industry’s larger effort to shorten study times.

To make ePRO successful at scale, sites say that technology must be simplified, and training streamlined

 so they can easily troubleshoot for their patients and avoid third-party help-desk support. Intuitive apps, easily digestible administrator and user training, as well as sandbox environments that allow for administrators to test different emergency scenarios would go a long way.

Give patients more options: It’s worth the effort

Just as consensus is growing around the importance of patient optionality in clinical trials, it is also growing around ePROs being a core part of that goal. One cannot exist without the other. Besides the data quality advantages, ePROs also elevate patients from test subjects to partners in drug research—minimizing the burden on patients and their caregivers while amplifying their voices.

Life sciences companies, and clinical research in general, cannot revert to inefficient pre-pandemic processes. Even as the economy threatens budgets, the industry can’t afford to take its eye off the progress we’ve started to make with patient-centric decentralized trials. Modern ePRO solutions offer a clear path forward. If we can successfully make ePRO a configurable, enterprise-grade solution that provides sponsors with additional flexibility, the result is digital transformation around the patient experience that also accelerates study timelines. The harder we step on the gas, the more quickly we can reach scale. It will take work—but the benefits will be more than worth the effort.

, vice president and general manager of eCOA, Medable

 Core Patient-Reported Outcomes in Cancer Clinical Trials

https://www.fda.gov/regulatory-information/search-fda-guidance-documents/core-patient-reported-outcomes-cancer-clinical-trials

Tourangeau, R.; Yan T. Sensitive Questions in Surveys. 

https://www.researchgate.net/publication/6117379_Sensitive_Questions_in_Surveys

Byrom, B.; Doll, H.; Muehlhausen, W. Measurement Equivalence of Patient-Reported Outcome Measure Response Scale Types Collected Using Bring Your Own Device Compared to Paper and a Provisioned Device: Results of a Randomized Equivalence Trial. 

https://doi.org/10.1016/j.jval.2017.10.008

Returning Clinical Data to Patients. Pfizer. 2023, 

https://www.pfizer.com/science/clinical-trials/trial-data-and-results/data-to-patients

https://confluence.hl7.org/display/VA/Vulcan+Accelerator+Home 

eCOA Digital Week: An Honest Assessment of eCOAs From a Site Perspective. Medable webinar. May 17, 2023, 

https://www.medable.com/knowledge-center/on-demand-webinar-ecoa-digital-week-an-honest-assessment-of-ecoas-from-a-site-perspective

How advanced ePRO tools and approaches offer a path forward.

Clinical Trial Optionality: Its Reality is Within Reach

Clinical trial participation is the most important factor to enable pharmaceutical, biotech, and medical device companies to move their medicines and treatments forward to improve the health of all of us. We all know someone that is on a medicine that was not available 30 or even 15 years ago that have had amazing benefits for those patients. Everything from a statin for cholesterol to cancer treatments like Keytruda, Opdivo, and Tagrisso.

We all know how difficult it is to recruit people into a clinical trial, and we all read that on average the dropout rate of a study is around 30%. Why is that? And what can be done about it? 

I believe that there is a macro problem that needs to be solved. And that is that we as a global society do not treat clinical trial participants like the heroes they truly are. Make no mistake about it, being a clinical trial participant, especially in an interventional trial, is a sacrifice of time and body and represents a true commitment on the part of the patient. 

Like many of you, I have traveled extensively for business, and one day it hit me. After passengers needing extra time, the next group to board a plane are active military. I have no issue with that—those people are making a sacrifice, but why not clinical trial participants? That is also a sacrifice for the greater good.

Beyond the macro problem mentioned above, we have an opportunity to keep patients more engaged once they are in a trial—and potentially lower the dropout rates. Reasons for the high rates have been cited as inconvenience, complexities of the trial, fear and anxiety, lack of appreciation, and scheduling conflicts. Also, once a patient is in the study, how do we keep that individual compliant so, at the end, the data that has been collected is complete and usable in the analysis?

We currently have technology available to us to make this retention and compliance much better. An increasingly larger number of trials are using patient-reported data as part of primary and secondary endpoints. A large portion of this data is being collected directly by the patient away from the clinic and usually on a device that they have—either their own phone or one that was provided for the clinical trial.

This gives us the opportunity to improve engagement, compliance, and, as a result, retention. In addition to telling the patient what questionnaire (or instrument) they need to fill out when, we can also ask the question, “What do patients need to be willing and active participants in the trial?”

We can answer that question by providing information about the trial; we can ask questions that are not clinical in nature to find out if the participant feels valued, or if being in the trial is a burden to them or their family.

And, most importantly, we can say thank you each step of the way.

This sounds simple to do, but it is not. The current state of play from most providers of electronic patient-reported outcome (ePRO) tools are just to deliver the text-based questionnaires and perhaps a reminder or two to fill them out on time.

These companies typically do not approach the problem with the right level of empathy for the patient, as to what they are going through to be in the trial.

In order to keep the trial participant more engaged in the study, we need to give them an experience that is more akin to the apps that they use every day.

This is a topic that can be discussed for hours. But I do believe we are at a point that a huge difference can be made—on behalf of the study participant—with technology and a recognition of the sacrifice that is being made for the greater good.

A Closing Thought

Steve Rosenberg, CEO of uMotif gives commentary on providing clinical trial patients with the best experience possible.


Trial Participants Are Heroes, Let’s Treat Them That Way

During the past three decades, reliance on outsourcing has steadily increased as sponsor companies seek capacity and scale, scientific and executional expertise, cost containment, and operating efficiencies.

There are many indicators of the magnitude of sponsor demand for, and dependence on, contract service providers. Outsourcing has been the single largest category of clinical development spending by sponsor organizations since 2017. At the present time, spending by pharmaceutical and biotechnology companies on contract clinical services is growing at three times the rate of spending on sponsors’ own internal development operations and infrastructure (6-8% annually).

And there are no signs that demand for outsourcing will diminish. We have seen a proliferation of smaller pharma and biotech companies with at least one drug in active clinical testing. For these organizations, outsourcing meets a critical need for personnel, experience, and expertise. The year 2023 has also been experiencing a global economic downturn with major and mid-size pharma companies—such as Amgen, Biogen, Bristol Myers Squibb, Novo Nordisk, and Takeda—responding in part by consolidating operations. The high volume of drugs in the global R&D pipeline continues to grow, signaling that variable capacity obtained through outsourcing will be an essential near-term drug development operating strategy.

Columns and articles that I have authored and co-authored in 

over the yearscharacterize the wide variety of outsourcing models that sponsor companies are implementing. Outsourcing models have become increasingly complex operationally, with more intermediary service providers and more data-intensive and technology-enabled elements. Pharma and biotech companies are implementing ever-more customized approaches to engage with collaborative partners. Sponsors are mixing and matching internal and contract staff across functions, varying use of full-service and functional service arrangements—and many variations in between—largely on a clinical-trial- by-clinical-trial basis.

A study recently completed by the Tufts Center for the Study of Drug Development (Tufts CSDD) of approximately 120 late-stage pivotal trials shows that functional service provider-sourcing strategies were most commonly used (30-40% of pivotal trials) to support clinical monitoring, data management, and statistical analyses. Core areas most commonly kept in-house include site initiation and study start-up, overall project management, and medical writing. Many other sourcing strategies were used with relatively low and variable frequencies, including full- and multi-service, blended, embedded, and staff augmentation models.

Wide variation on sourcing model use was also observed across therapeutic areas with full-service models most commonly used in oncology (30% of pivotal trials) and functional service provider models used in non-oncology pivotal trials (58% of pivotal trials).

As complexity and customization in contract service provider usage has increased during the past several decades, sponsors’ capacity, experience, and expertise needs have largely been met. But improvements in drug development operating performance and economic outcomes have been elusive.

Overall drug development program speed, cost, and success rates have not gotten better—they have all worsened during the past 30 years. We have observed a higher incidence of unplanned delays and out-of-scope activity. Clinical trial durations across all phases have lengthened with high and wider variation observed around mean durations, suggesting less consistent and predictable cycle time performance.

Many factors contribute to this long time horizon of operating performance shortcomings, delays, and inefficiencies. Sponsor-CRO collaborative effectiveness has played a part. R&D executives in both sponsor and CRO companies have long lamented relationship attributes such as the lack of trust, poor communication, and the lack of senior management support as major contributors. These perennial conditions have been difficult to identify and address, as they are intangible and diffuse.

Tufts CSDD has been looking at other, more tangible and objectively measured “structural” relationship attributes. Last year in 

 we proposed that specific underlying structures may be associated with and predictive of collaborative efficiency and effectiveness. These structures include the systems and processes used to support a given collaboration (i.e., sponsor vs. vendor) as well as the primary party accountable for functional timelines and deliverables.

The results of a pilot study will soon be published showing that these structural characteristics are measurably associated with performance and economic outcomes.

In this pilot study, Tufts CSDD had sponsor companies define specific outsourcing models used on individual late-stage clinical trials. Sponsor organizations also provided performance measures associated with each clinical trial.

The Tufts CSDD research team has recently turned its attention to pre-existing relationship structures—cultural and infrastructure—that are likely contributing to performance inefficiencies. These pre-existing structures often render parties incompatible and introduce interactive tensions at the outset of a collaboration. They include: decision-making hierarchies such as functional or matrix operational designs vs. decentralized approaches organized around therapeutic area; risk aversion and how parties plan for and manage operating risk; each party’s conflict escalation and resolution handling processes; and the ways that collaborative parties manage, access, and transfer data.

There are no doubt other areas to include in this list of pre-existing structures and processes that are brought to a given collaboration. We welcome comments and additions to this list. During the next 12 months, Tufts CSDD plans to look at these specific structural attributes and their association with clinical trial performance and economic outcomes.

Knowledge of the role that pre-existing attributes play on collaboration inefficiencies may inform other relationships, including joint development arrangements, in-licensing deals, and mergers and acquisitions. Indeed, in the wake of the pandemic, the volume and scale of M&A activity has been rising with a number of highly notable deals during 2022 and 2023, including Pfizer’s $43 billion purchase of Seagen; Amgen’s $28 billion acquisition of Horizon Therapeutics; and Merck‘s $11 billion purchase of Prometheus Biosciences. The rise of such transactions makes early assessment of misalignment areas critical.

Ultimately, learnings and insights drawn from research on culture and infrastructure-based collaboration attributes will inform new operating practices and processes, vendor qualification strategies, and staff training. 

, MBA, executive director and research professor, Tufts Center for the Study of Drug Development, Tufts University School of Medicine

Clinical Trial Insights

Investigating more objectively measured 'structural' characteristics.

In Search of Attributes Predictive of Collaboration Effectiveness

Key risk indicators (KRIs) are commonly used as an important component of central monitoring to enable study teams to detect emerging quality issues of interest during a clinical trial. Given the large volume of KRI-related information now available for global clinical trials on the CluePoints risk-based quality management (RBQM) platform, we decided to take the opportunity to assess to what degree this KRI data provides evidence of the disruption of the war in Ukraine to clinical research being conducted there.

We assessed the impact of the war in Ukraine across 10 commonly used KRIs in drug development, covering several categories of study risk (compliance, timeliness of data reporting, enrollment and retention, and safety).

KRI results from investigative sites located in Ukraine were compared with KRI results from sites in all countries globally. The analysis utilized the following scope of information:

Overall rate of significant KRI results for sites located in Ukraine has consistently risen since 2021.

Applied Clinical Trials-09-01-2023

The Pulse of Modeling and Simulation in Clinical Trials

Executing Trial Design With Patients—Not For Patients

Clinical Trial Optionality: Its Reality is Within Reach

Trial Participants Are Heroes, Let’s Treat Them That Way

In Search of Attributes Predictive of Collaboration Effectiveness

Ukraine War's Impact on Clinical Research: Evidence from Key Risk Indicators

eCOA Systems and Intended Use: Key Considerations

Harnessing the Value of Data Intelligence in Clinical Trials

Staying Ahead of the Game

The Case for Loosening Trial Eligibility Reins

Speeding Up European Clinical Trials in Response to Emergencies