Within the domain of clinical trials, the goals of quality by design (QbD) are to assure that the outcome data are fit for their intended purpose and that there is an absence of errors that could impact decision-making. Since all products and services within a clinical trial are subject to regulatory oversight, all sponsors, vendors, and clinical research sites must always be prepared for that knock on the door when your administrator says, as mine said to me a few years ago, “The FDA has arrived and would like to speak to the person responsible for the clinical trial.”

A frequently asked question is what have been the barriers to entry preventing pharmaceutical and medical device companies from widely adopting and implementing paperless and digitally based clinical trials? Ironically, for more than a decade, technologies allowing for paperless and digital clinical trials have been encouraged by regulators. Fortunately, the time has finally come for the transition to a global acceptance by the pharmaceutical industry and regulators of the use of paperless and digital technologies to collect clinical trial data both within the clinic and remotely.

The FDA and others have issued multiple guidance documents addressing the use of digital technology tools when used in clinical trials. Now that the electronic world has finally entered the clinical trials space, as part of routine pre-approval inspections, there must be stakeholder assessments of the impacts of digital systems on 1) informed consent; 2) data collected to determine effectiveness; 3) patient safety by assuring that all safety events are accurately collected and reported 4) data integrity and accountability; 5) protocol compliance; 6) monitoring of the study, including risk-based monitoring; and 7) drug/device accountability etc.

Since digital-based companies have not been routinely subject to FDA laws, rules, and regulations, the following are key elements to be addressed and assessed by these organizations choosing to enter the clinical trials space.

 The basic goal of software validation is to assure that configured applications act as intended when used in clinical trials. Therefore, when regulators inspect software products, it is critical to document that the trial software applications are validated. Fortunately, starting in 2002, the FDA has provided validation guidelines for software supporting regulated products. For new products hitting the market, an assessment should also be made if FDA clearance of the software would be required through the device 510(k) or PMA regulatory approval routes. For global programs, local regulatory requirements must also be addressed.

 These are elements that must be considered during development of digital devices for use in clinical trials. FDA has issued a guidance document to assist the industry on how to follow appropriate human factors and usability engineering processes so that new medical devices will be safe and effective for the intended users, uses, and use environments. It is recommended that these studies focus specifically on the user interface, including displays, controls, packaging, product labels, and instructions for use. As part of their design controls, manufacturers must also conduct a risk analysis that includes the risks associated with device use and the measures designed to mitigate those risks.

 Some of those fully committed to using paper records often ask the question, if a system is fully electronic how do we know that the data are real if there are no paper source records to compare with the electronic record? To assuage some of these concerns, as part of a company’s SOPs, there must be supporting documentation that the vendor has been qualified and that the software is in a validated state. It is also critical that all roles and responsibilities are properly identified, as well as how the original source data are controlled. This includes the presence of date and time stamps for all transactions, and an audit trail of changes cannot be overwritten.

 One of the first things the inspectors may request is to talk to the head of quality as well as who was directly responsible for the trial. Be prepared to share your organizational chart as it relates to the study and copies of relevant contracts/agreements, although inspectors are usually not concerned about financial matters. The inspectors may also ask for the documents supporting vendor qualifications, software validation/testing, and risk mitigation plans.

Articles

A Closing Thought

Tech companies entering space must adapt and adhere to regulations set by FDA.

Regulatory Considerations for Digitally Driven Trials

Since 2017 many clinical trial vendors have developed products aimed at improving patient recruitment for clinical trials, tracking patient responses to potential therapies, or integrating all data produced in the trial technology ecosystem. The COVID-19 pandemic hastened these changes, especially related to the technologies that support decentralized clinical trials, although some observers say that many technologies now coming into view existed pre-pandemic.

“The pandemic triggered not a digital revolution, but an attitudinal revolution in the pharmaceutical and life sciences industry. Decentralized clinical trials (DCTs) were a fashion statement two years ago, but now everyone has a strategy,” says Nimita Limaye, research vice president for tech-focused market research firm IDC Health Insight.

“There’s been huge growth in things like wearables and sensors, which are really mapping how patients are responding to interventions in a very subtle way,” observes Limaye. Thanks to these sensors, it’s now easier to tell if patients are taking prescribed medications, or trial participants are maintaining experimental treatments, she says. Limaye adds that the practice of e-consenting patients has become much more routine in the last five years, along with using machine learning to detect safety signals that might not be captured in a structured adverse event report but are hidden in the more idiosyncratic narrative records of the trial.

“Extracting intelligence from structured and unstructured data is an important use case” for machine learning, says Limaye. Another is to use machine learning to mine real-world data about how different populations fared on FDA approved drugs post-approval, to build synthetic control arms for future clinical trials in which real-world experience on the already approved drug is compared head-to-head against the new intervention.

Machine learning depends on training algorithms on large sets of data, Limaye notes, but many pharmaceutical companies securely protect their own clinical trial data in a way that’s impenetrable to machine learning algorithms. Limaye adds that federated learning technologies, in which algorithms access data that never leaves its secure location, are emerging solutions to this problem that pharma companies are beginning to embrace.

Overarching these technological developments is a broad sense among clinical trial observers that there’s a once-in-a-lifetime opportunity to infuse equity and inclusion into the design and deployment of clinical trials that use digital tools. Clinical trial participants, today, are not representative, which means that the results of clinical trials are not always generalizable.

“When we pick up these tools—whether that’s the use of real-world data, whether it’s running [artificial intelligence] and machine learning techniques on these data, whether it’s the use of telehealth for virtual visits, whether it’s the use of digital technologies and remote patient monitoring—it’s not enough to just say we’ll do it and we’ll figure out the equity bit afterwards,” says Jennifer Goldsack, CEO of the nonprofit Digital Medicine Society (DiMe), which aims to promote digital-first clinical trials that could not have been done in the analog world.

“We could be encouraged by how the current process works, when we talk about the history of the informed consent process,” adds Jude Ngang, executive director of representation in clinical research at Amgen and a DiMe partner in a new initiative to develop more inclusive clinical trials. According to Ngang, Amgen takes great pains to ensure that everyone, regardless of language or literacy level, understands what they are agreeing to as they begin a clinical trial, and says the same care can be taken with ensuring that all clinical trial participants know how to use a wearable sensor or other digital device.

Part of solving the trial recruitment puzzle is simply to alert more people about relevant trials. Amgen has invested in CancerIQ, a genetic risk assessment software program linked to patient medical records (EMRs). Many people go to community oncologists, not academic medical centers where frequent clinical trials occur, notes Ngang. With CancerIQ, a community oncologist can alert patients about enrolling studies for new cancer treatments they might not have learned about otherwise.

CancerIQ is just one of many clinical trial-related technologies. This article will detail other emerging tools that enhance clinical trial recruitment, improve participant monitoring once a study is underway, or aim to integrate clinical trial data in more impactful ways. Read on to learn about the brand new kids on the block, or the old kids who’ve learned how to pivot.

Linking patients, improving engagement, and novel measurements

“There’s this idea that doctors are afraid of losing patients to clinical trials, and that’s why they don’t refer them, but that’s not true. The main reason physicians don’t tell patients about trials is because it takes extra time,” says Mukul Mehra, a gastroenterologist and co-founder of IllumiCare. The company aims to make clinical trial referral a “prescribable event,” in the same way that a physician might prescribe a statin and then rely on existing infrastructure to get that drug to the patient.

Mehra says that trial notification systems do already exist within electronic health records (EHRs), but as email inbox messages that physicians might not read until hours or days after seeing the patient. At that point the physician needs to call up the patient record and triage whether they are eligible for the study, which is actually a better task for clinical trial coordinators.

IllumiCare’s “Smart Ribbon” technology surfaces matched trials within the EMR workflow, as the doctor is seeing the patient, and works with all major EHR platforms. A doctor can either discuss the trial with the patient on the spot, or refer them to a clinical trial coordinator to help them decide whether they’d like to enroll. Just launched in May for this purpose, after 24 months of development, Mehra expects that Smart Ribbon will make clinical trial recruitment and discovery easier.

Enrolling patients into a trial is just one challenge. Preventing attrition is another.

“How can we improve the participant experience in a meaningful way?” asks Andrea Valente, CEO of the clinical trial technology vendor ClinOne, whose prime client base are biopharma sponsors. Valente says that the average cost per patient of an oncology trial is $100,000, and up to $300,000 when involving a rare disease patient. Replacing patients who drop out mid-trial is expensive, Valente notes, and could lead to delays in availability of new treatments.

Much of ClinOne’s innovation in recent years is due to COVID-19, although the company pre-dates the pandemic. During the pandemic, e-consenting and video consults bloomed, as many ClinOne clients began to offer DCTs. Interactive SMS messages to remind patients to take medications also became more common, and a partnership between ClinOne and Uber Health offered transportation to patients who were unable to drive as a condition of enrolling in a study for a major depressive disorder treatment.

ClinOne found that, at some trial sites, 98% of elderly patients in a cardiovascular drug trial were successfully taking medications 18 months after the trial began, due in part to the medication reminders; and that offering free transportation (up to two round trips a day within a 200 mile radius, for personal and not only trial-related reasons) significantly enhanced enrollment in the depressive disorder study. ClinOne and the trial sponsor expect that retention in this study will be enhanced as well, thanks to the transportation solution.

Medical sensors and wearables, which people wear regularly as they move about their daily lives, open up new possibilities for clinical trials that the five-year-old company ObvioHealth aims to explore. Start with the humble cough.

“It sounds funny, but everyone’s cough is different,” says ObvioHealth’s CEO Ivan Jarry, who is working on a project that uses AI to detect hidden health risks within someone’s coughing patterns. This could ultimately inform new clinical trials, once clinicians and researchers have a better understanding of the health implications of different coughing patterns.

Meanwhile, ObvioHealth has developed a platform that continuously collects data gathered from wearables such as blood pressure levels, oxygenation levels, or heart rates of patients in clinical trials for different treatments—with the data fed to the research site in real time (or as soon as possible if the patient loses connectivity). ObvioHealth doesn’t just transmit the wearable data wholesale to the researcher, but also uses algorithms to detect if there are safety signals, and sends the results of this analysis, too. Its clients include pharma and biotech companies, contract research organizations (CROs), and academic medical centers.

For example, if a patient’s blood pressure always plunges right after receiving an experimental treatment, either that treatment is a total non-starter or can only work if the patient takes precautions. In the analog world, such a relationship between the medication and blood pressure might take weeks or sometimes months to detect.

“Our aim is to detect actionable signals from data,” says Jarry. ObvioHealth also wants to be a leader in women’s health studies, which Jarry notes have historically been understudied or studied with invasive procedures. This year, ObvioHealth was the research vendor for a fully virtual study that concludes that digital therapeutic devices improve the effectiveness of home pelvic floor muscle training (kegels) for reducing urinary incontinence symptoms in women.

Integrating data from every possible source

Several clinical trial technology companies focus on the so-called “back end” of digital clinical trials, or the data management and integration needed to make these studies possible.

KORE has existed since 2002, but just this year the organization acquired the companies Business Mobility Partners and SIMON IoT (internet of things) to enhance its connected health offerings. The acquisition helps KORE to serve high volume, Phase III clinical trials that can involve up to 3,000 participants, according to KORE’s Chief Marketing Officer Landon Garner.

IoT means that a physical object, like an internet-enabled blood pressure monitor, is linked to other connected objects such as a handheld device that records and communicates results (in this case, the blood pressure readings). IoT is why a programmable thermostat, say, can also turn on a HVAC system. And IoT is the reason why scientists and regulators moved from a COVID-19 vaccine concept to placing vaccines in arms within 12 months, Garner says.

“The pandemic became a forcing function for virtual or decentralized trials, using IoT technology,” says Garner, because the connectivity afforded by IoT technologies enabled real-time data capture that showed the efficacy of now-approved COVID-19 vaccines. The same flexibility is available to any company that wishes to run a global drug trial.

“We have access to 600 mobile networks around the world,” says Garner. According to the executive, KORE can provide tools such as digital weight scales or continuous glucose monitors that will work in any trial location. KORE’s technology does not analyze the data generated by a trial directly—that’s the work of algorithms and the clinical trial research team—but instead it federates the various data streams so that they all go to the right place.

The healthcare technology company Seqster created SeqsterOS to capture patient-matched EHRs, genomic DNA, wearables, and pharmacy and lab test data collected during a clinical trial, via its dashboard. Trial participants authorize with whom they share this data, in a HIPAA and FDA-compliant manner that also complies with European privacy regulations, according to Seqster CEO Ardy Arianpour. Once the trial participant authorizes sharing any of their data with vetted Seqster users, it is available.

“We’re at the tip of an exploding patient-centric and digital clinical trials industry,” says Arianpour, who co-founded Seqster in 2016 and launched an early version of its research portal in 2018. Seqster has continuously iterated its operating system since then, with the aim to “break down health data silos at scale,” in Arianpour’s words. Its most recent software integration, announced in April, is with Salesforce Health Cloud.

For health outcomes researchers interested in how economics affects participation and retention in clinical trials, Seqster includes socioeconomic determinants of health information, such as a trial participant’s zip code or annual income.

“Your zip code is one of the most important determinants of health, and most people don’t know that,” adds Arianpour. Seqster’s goal is to integrate every possible source of data relevant to someone’s health while in a clinical trial or as they are receiving care, and to enable algorithmic and human analysis of these various data streams.

Data curation and management will only become more critical to clinical trial deployments and healthcare delivery, contends Limaye, who notes “we’re a data-hungry world,” particularly when it comes to harnessing personalized data. For example, the ability, she says, to capture not only how people’s heart rate fluctuates or glucose levels vary, but also to sequence individual genomes or proteomes or lipid levels.

One challenge today, however, is that much of this data resides in silos, either in the purview of a clinical trial sponsor or EHR or a third-party provider. Limaye argues that developing holistic and longitudinal views of a patient’s health requires developing interoperable protocols that enable data flow between systems while respecting data security, privacy, and sovereignty.

At the same time as data volume is increasing, the proportion of hard-to-interpret medical data is rising as well. IDC predicts that less than 5% of all data generated electronically will be “analyzable” by 2025, meaning that many data-driven health insights will be hard to derive with current tools.

One exciting data analysis tool Limaye foresees is “codified listening,” in which machine learning and speech emotion-recognition algorithms provide new insights into the emotions or sensitivities that drive the behavior of patients or clinical trial participants. A patient might leave a voice memo that such software decodes. This technology is in its early days, Limaye cautions, but could open new doors to understanding the patient or trial participant experience.

Meanwhile, the distance between the clinical trial site and point-of-care delivery is shrinking. “There is a growing confluence between healthcare and life sciences,” says Limaye, noting that pharmacy giants such as CVS and Walgreens are becoming clinical trial sites, not just places for dispensing medications that were approved through an entirely separate clinical trial process. Limaye foresees an eventual collapse of the distance between healthcare delivery and clinical trial sites. Goldsack agrees.

“When you have to lock the data in a filing cabinet and only the [principal investigator] or the office manager has the key, yeah—you can see why that happened,” says Goldsack, in describing the historical separation between the clinical trial and healthcare delivery spaces. “But we are so far from that reality now.”

New Vendors in the Clinical Trials Industry Discuss Their Products

Feature Articles

COVID has accelerated to the scene upstart digital efforts to transform patient engagement and data management/integration—but progress remains measured.

Clinical Trial Technology: New Kids on the Block are Changing Industry Dynamic

It is widely recognized that the clinical research enterprise embraced numerous changes and adaptations during the COVID-19 pandemic to maintain patient safety, accelerate the development pathway, and ensure clinical trial continuity. Topping the list of changes is the unprecedented number of protocol deviations that have been implemented since the global lockdown.

Sadly, we do not have empirical data at this time characterizing the expected sharp increase in the average number of deviations per protocol. However, my team at the Tufts Center for the Study of Drug Development (Tufts CSDD) has recently completed a study capturing pre-pandemic protocol deviation metrics from which future comparisons can be made.

Typically, protocol deviations are executed by study staff while the clinical trial is underway in an effort to accommodate study volunteers encountering difficulties complying with the schedule of visits or adhering to study medication administration requirements. Investigative site personnel may also deviate from the protocol when facing difficulties finding eligible patients, and following and performing clinical and administrative procedures dictated by
the protocol.

Most deviations have a minimal impact on protocol integrity and execution. A subset of all deviations, however, are more significant and may harm the quality of the research, may put the rights and safety of study volunteers at risk, and may detract from the completeness, accuracy, and reliability of the study data. There are a number of useful resources to help organizations identify, classify, and manage protocol deviations including those by the Drug Information Association’s (DIA) Good Clinical Practice and Quality Assurance community and by TransCelerate BioPharma.

Finally, its important to note that protocol deviations are the top cause of FDA inspection warning letters. Approximately one-third (30%) of all warning letters are due to the failure to follow the investigational plan. Site failure to adequately maintain case report records is the second most common reason, accounting for 17% of all warning letters.

To our knowledge, little-to-no data exists quantifying and benchmarking protocol deviation experience overall and by clinical phase. For this reason, Tufts CSDD engaged a working group of 20 major and mid-sized pharmaceutical companies and contract research organizations (CROs) to gather hard data on deviations associated with traditional clinical trial protocols (i.e., master protocols and adaptive designs were not included). Our study sample included protocols that had received final approval between January 2013 and December 2018 and had a primary completion date or database lock date before December 31, 2019. The data-collection process deployed in this study followed an approach that Tufts CSDD has been using to evaluate trends in, and the impact of, protocol design practice since 2008.

Data on a total of 187 protocols targeting a range of disease conditions was analyzed. One-quarter of the total were Phase I protocols, 39% Phase II, and 36% were Phase III protocols.

The table on the facing page provides high-level baseline measures of protocol deviation experience by clinical phase. Phase I protocols have the lowest mean number of deviations and involve half the proportion of patients than those observed in Phase II and Phase III protocols. Each Phase III protocol has a mean number of 119 total deviations, involving approximately one-third of all patients participating in that protocol. The coefficients of variation around the mean number are very high—between 1.7 and 2.0—indicating that the average deviations per protocol are widely dispersed and difficult to predict.

Wide variation in protocol deviation experience was also observed by therapeutic area. Phase II and III oncology protocols average almost 20% more total deviations than the average for non-oncology protocols, at 108.8 and 91.9, respectively. Protocol deviations in combined Phase II and III oncology protocols involve 47% of the total study volunteers, nearly double the proportion observed in non-oncology protocols. Rare disease indications average a lower relative number of total deviations (78.1) among a smaller proportion of study volunteers (27.7%) compared to that of non-rare disease indications.

Tufts CSDD also found that the incidence of protocol deviations was significantly, positively correlated with all clinical trial milestone durations, with the exception of study start-up and study close-out. The duration to complete all first-patient visits (first patient first visit to last patient first visit), the overall treatment duration (first patient first visit to last patient last visit), and the total clinical trial duration from protocol approval to database lock showed weak but significant positive correlations with the number of protocol deviations per protocol.

We were not surprised to find that no association existed between the incidence of protocol deviations and study start-up and close-out cycle times. Protocol implementation burden is generally low during the study initiation process. This burden, however, for both study staff and study volunteers, becomes more pronounced as these parties must comply with protocol implementation requirements over time.

A relationship between select protocol design complexity variables and the incidence of protocol deviations was observed. The number of endpoints and the number of procedures per visit were modestly positively associated with, and predictive of, the incidence of deviations per protocol. Adjustments to these select design variables may play a role in preventing protocol deviations and in minimizing their risk to the study and data integrity.

Two operational variables were also positively associated with protocol deviations. The number of countries showed a modest relationship. A positive relationship was also demonstrated between the number of investigative sites and the number of protocol deviations, suggesting that decisions and behaviors that diverge from the investigational plan may be a function of variation in investigative site personnel training, site infrastructure, operating environment, and culture.

Tufts CSDD plans to build on this body of research. We will be looking at the root causes of protocol deviations and how to address them. Future research will also look at deviations with more granularity to understand the incidence and impact of minor and major deviations. Whereas some deviations may improve participation convenience and clinical trial executional feasibility, other deviations may pose substantially greater risk to study volunteer rights and safety and to the quality and integrity of the clinical trial data. We also hope to understand the relationships between types of deviations and patient characteristics—including demographic subgroup and geographic location—and investigative site characteristics.

The results of this initial study provide useful measures that sponsors and CROs can use to benchmark their own protocol deviation experience and identify factors most associated with protocol deviations. 

Clinical Trial Insights

Study uncovers pre-pandemic deviation levels.

Quantifying Protocol Deviation Experience by Clinical Phase

While the COVID-19 pandemic has undoubtedly catalyzed the adoption of more flexible data- collection approaches within clinical trials, this has been the direction of travel for some time. Rather than bringing participants into sites, allowing them to participate from the safety of their own homes by leveraging remote data-collection methods placed participant preference at the center of the research effort and positively impacted participant engagement. The use of electronic methods to capture clinical outcomes assessment (COA) data has facilitated this pivot, as has the use of telehealth applications. However, some cognitive assessments—such as the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment(MoCA)—require complex interactions between the participant and clinician to assess dementias and other neurocognitive disorders. These cognitive COAs are more difficult to administer remotely and require creative solutions to accommodate a cognitively-impaired population.

When choosing a cognitive assessment, it is important to consider whether the assessment is both appropriate to the population being investigated and that it will address the hypothesis and fulfill the criteria for the trial estimand. Another consideration is whether the assessment license holder will allow it to be performed remotely. There are several less complex cognitive COAs that more easily translate to a remote environment and indeed some are specifically designed for this purpose. However, these “less-complex” assessments may not satisfy the needs of the trial or be suitable for the population.

Telephone-based visits can accommodate some cognitive COAs, but many validated and standardized examples commonly used in clinical trials include a combination of clinician interview and participant performance tasks (e.g., drawing shapes, completing diagrams, or behavioral tasks). Under traditional administration conditions, the clinician and participant sit facing each other. The clinician can observe the participant’s behaviors and task completion. Drawing, writing, or diagramming tasks are typically done on paper, which becomes a source document and must be stored in the participant’s study file; this is not practical under remote administration conditions. Source documents must be dated and signed off to ensure they are compliant with ALCOA+ principles. In fact, to be truly ALCOA+ compliant, paper source records should always be “available” to the study investigator even before later delivery to the site. Photo images of paper records cannot be considered source. Despite these obstacles, there are creative solutions available for administering cognitive COAs remotely.

An approach that eCOA Consortium members have seen work in persons with dementia is by combining a secure telehealth system with an eCOA application. Clinicians and participants (assisted by study partners) are provisioned with tablet devices (with styli) that allows them to interactively write or draw directly on to the tablet screen when required. The clinician initiates the interview and records responses on their tablet. When it is time for the participant to perform the drawing task the clinician can “hand-off” access to the participant. The participant logs in with their own unique ID and uses the stylus to complete the task on their tablet device. The image created by the participant becomes the source data record and is saved directly to the server along with the associated meta-data that ensures compliance.

The telehealth system also allows the clinician to view and rate other participant behaviors, such as following commands and object naming that are common to these types of COAs. When migrating cognitive COAs to digital format, best practices guidelines should be followed. For example, when using the Repeatable Battery for Assessment of Neuropsychological Status (RBANS), digital drawing would be classified as a Level II change that would require psychometric testing.

Regardless of the pandemic, solutions like these are proving beneficial for all research stakeholders. Participants, study partners, and clinicians are all able to perform critical activities remotely. This approach focuses on the participant and is driven by science and technology, enhancing participant recruitment and retention, and reducing participant burden.

Authored on behalf of Critical Path Institute’s eCOA Consortium by 

, Principal Scientist, Clinical Ink; and 

, Senior Scientific Advisor, eCOA, WCG – MedAvante-ProPhase. The eCOA Consortium wishes to acknowledge the contributions of 

Pandemic aside, remote solutions are advancing care in clinical trials.

Administering Complex Cognitive Tests Remotely in The Cognitively-Impaired

Martin Gouldstone, Global SVP, business development, Owkin, speaks to 

 on the use of artificial intelligence (AI) in data practices and approaches for clinical research, and the distinction of “ethical AI.” Gouldstone was previously chief business officer of life sciences for Sensyne when this Q&A was conducted, as part of an 

: Can you explain what ethical AI and ethical data means?

 Ethical AI and ethical data sharing is essentially a trust model for patient data. Organizations that generate high-value findings—in particular, the analysis of patient data—describe the way that the technology works to both protect the identity of the patient, but still generate incredibly valuable analyses and insights from the patient data sets. These can be used to drive the successful execution of new drug development, while ensuring that patient identity is secure and privacy is secure.

: We are hearing more and more about the use of synthetic control arms in clinical trials. Why do you think that is?

 I think the reason is that we’ve reached a kind of inflection point where both the technology and the ability to handle large data sets and the access to the data have come together and enabled synthetic control arms to be possible. Before, there just wasn’t the technology to support managing the data. For instance, all of our work is done in the cloud. We don’t have racks of big servers anymore. And that has enabled us to manage rapidly and integrate large data sets. I think the other challenge that the industry faces or has faced is data interoperability. Getting the [numerous] data sets to communicate is a real challenge.

: Can you explain the difficulty of integrating all that data together to make more sense?

 A patient’s health record has many different components to it; some are called structured data, and they’re quite simple—for example, a prescribed medication, the data is the medication name and the frequency of dosing. But then there are doctors’ notes, which are unstructured data; imaging data, including X-rays, CAT scans, MRI scans; pathology reports, which are in PDF format. So multiple formats, multiple information sources, all on one patient, probably in different locations. And you’ve got to make sense of all of that and bring it all together related to the patient—all de-identified data that has to be anonymized, aggregated into datasets that makes sense for disease areas, indications, and more. Then there’s also outcomes, which is probably the biggest value driver of the data from my perspective for pharma. So bringing all of that together in a common data model is hugely challenging.

: What do you think is the most significant change in drug development in the past five to 10 years, or the past five years?

 Early in my career, I was in clinical research. I started my pharmaceutical career carrying the bag for a year and then went into clinical research and ran clinical trials in areas as diverse as female health and oncology. The increased use in Wi-Fi-enabled technologies has made a big difference with processes including source data verification for [clinical research associates].

As I said earlier, this data revolution tied in with the development of the technologies to enable access to that data in a secure environment. And then be able to apply that to not just the clinical research process, but also the market access for new drugs, pricing models, etc.

I think there’s still a long way to go. The pharmaceutical industry has always been quite slow in adopting new technologies, particularly large pharma, because of scale. It costs a lot of money just to move to the next MS Word package, for instance, let alone change how you do processes. But I do think that is where these new AI technologies, such as machine learning, make a difference.

Martin Gouldstone, Global SVP, business development, Owkin, speaks to ACT on the use of AI in data practices and approaches for clinical research, and the distinction of “ethical AI."

Assessing Use of Data Integration and AI In Clinical Trials Today

If you haven’t had the opportunity to attend the Drug Information Association (DIA) Global Annual Meeting (GAM) held each year in June, this would be the year to do it. From June 19-23 in Chicago, this year’s DIA theme is “Innovation through Collaboration.” Tamei Elliott, senior manager of scientific programs with the DIA, said, “DIA has seen tremendous effort across the globe that has led to work-sharing initiatives, sharing of best practices and lessons learned, which then warrants the questions: ‘How do we sustain the innovation that has come out of the pandemic and what do we want to keep moving forward as a best practice?’” A DIA hallmark is regulatory authorities as presenters. This event will be no different and features town halls with FDA, Health Canada, Asia, PMDA, China, and a co-hosted Question Time with EMA and FDA.

Elliott noted that trends uncovered during the pandemic make up the topics of many of the key sessions. Those trends include global harmonization, collaboration, and cooperation; regulatory flexibility from the regulators; utilization of real-world data and real-world evidence; challenges in the recruitment of diverse populations in clinical trials; remote monitoring of clinical trials; expanded integration of digital and remote approaches; decentralized clinical trials; importance of patient engagement; and supply chain challenges.

Elliott said, “These topics encourage the importance of collaboration during the most challenging times, and through that, innovation evolves that then leads to new therapies and cures, as well as the creation of the supporting tools and processes that benefit the patients in need.”

, the sponsored article on patient centricity is the result of a roundtable held with key pharma experts. While all come from different-sized companies and therapeutic focuses, there was a consensus on what DCTs bring to the patient-centric table. These are best summarized by Jeremy Price, MBA, senior director, clinical innovation and strategic partnerships for Pfizer, who stated the company’s three pillars of DCT value are boosting study enrollment and retention; increasing diversity and inclusion in study populations; and creating a more patient-centric experience.

 is Editor-in-Chief of Applied Clinical Trials. She can be reached at 

From the Editor

Pandemic trends set stage for upcoming DIA Global Annual Meeting.

School’s Out for Summer, But Learning Continues

Issue PDF

Click the title above for a link to open the Applied Clinical Trials June 2022 issue in an interactive PDF format.

Applied Clinical Trials June 2022 Issue (PDF)

A new framework for extrapolating clinical trial data from adult studies seeks to facilitate the testing and approval of therapies for children more quickly and at less cost. The process for achieving such gains is presented in a draft guideline recently released by the International Council for Harmonization (ICH) that maps out common methods and strategies for identifying how development programs for adult drugs can inform pediatric indications. By establishing common regulatory requirements among regions, the initiative aims to speed the approval of and access to new drugs for young patients around the world, while limiting the number of children needed for testing in clinical trials.

The proposed pediatric extrapolation guideline (ICH E11A) was released by ICH in April to gain broader review and assessment by regulatory authorities and drug development sponsors around the world.

 This harmonization process began in 2017 and now appears on track to adopt a final guideline in 2024. An ICH assembly in Korea in November is slated to discuss this and several other proposed standards moving through the review process, such as quality risk management and analytical validation requirements, and newer projects on structured product quality, stability testing, and model informed drug development. In recent years, ICH has adopted guidelines on estimands and sensitivity analysis in clinical trials and on reproductive toxicology testing to support clinical studies; a newer initiative aims to update the E8 guideline on the conduct and reporting of clinical trials by incorporating quality-by-design principles into clinical research.

ICH also is moving forward with an initial roadmap for developing guidance on model-informed drug development (MIDD), considered a key approach for achieving a more efficient and more predictive clinical research process, with potential for reducing unnecessary patient exposure. The MIDD discussion group released a plan on March 31that lays out general principles, anticipated gains, time frames and options.

 The aim is to develop a general principles guideline in the next three-to-four years and a completed standard in 10 years. That would set the stage for revisions in several basic guidelines, such as on dose response information, population pharmacokinetic-pharmacodynamic studies, and disease progression modeling.

The ICH initiative on pediatric extrapolation was outlined at a public meeting on May 11 organized by FDA and Health Canada to update stakeholders on that and several other ICH projects.

 Lynne Yao, director of FDA’s Division of Pediatric and Maternal Health in the Center for Drug Evaluation and Research (CDER), described ICH efforts since 1994 to establish common global regulatory standards designed to provide children with access to medicines appropriately evaluated in adults and to support the inclusion of pediatric patients in product development programs where appropriate and feasible.

These initiatives for advancing pediatric clinical investigations have expanded since publication in 2000 of the initial ICH E11 guideline on pediatric clinical investigations. An addendum, E11 (R1), appeared in 2017 and recognized the need for more detailed standards on pediatric extrapolation. In response, an expert working group was formed to develop a reflection paper on the need and scope of an ICH E11A guideline on pediatric extrapolation. Initial steps involved aligning terminology and developing a systematic approach for considering study design, statistical methodologies, and modeling and simulation strategies in this area, Yao explained.

Now a draft, ICH E11A guideline provides a framework for utilizing pediatric extrapolation methods to meet regulatory standards for approval. It outlines a systematic approach for pediatric extrapolation studies that involves a full assessment and synthesis of existing data; quantitative predictions on the degree of similarity; development of a framework for potential reduction of required evidence; and later revision of uncertainties and assumptions. Pediatric extrapolation plans would consider patient response to treatment and identify gaps in knowledge involving dose selection, types of efficacy studies, and the need for a safety plan. The goal is to achieve more alignment among regulatory authorities on terminology and what study designs, statistical methodologies, and modeling and simulation strategies will advance pediatric extrapolation in drug development.

ICH standards for pediatric extrapolation, moreover, may help address broader concerns about the scope and extent of data extrapolation to support added indications for new drugs in patients not studied in original pivotal trials. While authorities generally agree that such extrapolation is valid and important for extending disease treatment to added patient populations, some experts question whether sponsors over-rely on limited clinical data to support added indications. This issue was highlighted in the recent debate over the extrapolation of limited clinical evidence in approving the Alzheimer’s disease treatment Aduhelm for patients with mild cognitive impairment. While experts agree that FDA and sponsors should have flexibility to expand an indication of an approved drug based on extrapolation of initial clinical data, pressure has increased for requiring post-approval studies to support such actions and to provide clear evidence of appropriate use.

https://database.ich.org/sites/default/files/ICH_E11A_Document_Step2_Guideline_2022_0404_0.pdf

https://database.ich.org/sites/default/files/ICH_E11A_Step_2_Presentation_2022_0404_0.pdf

https://admin.ich.org/sites/default/files/2022-05/ICH_MIDD_Roadmap_2022_0503.pdf

https://sbiaevents.com/files2022/ICH-Spring-2022-Agenda.pdf?utm_medium=email&utm_source=govdelivery

View from Washington

ICH draft guidance proposes standardized framework for identifying ways development programs for adult treatments can inform pediatric studies.

New Guidelines Aim to Advance Pediatric Drug Development

The protection of patients is being undermined by the European Medicines Agency’s (EMA) renewed reticence about releasing clinical data for independent review, according to an influential non-profit drug monitoring organization, which sees recent shifts to a more liberal approach falling victim to drug industry pressure.

Since it was set up in 1995, the EMA had come under attack from health campaigners, academics, and some prominent politicians over what they described as its excessive secrecy with the clinical trial data underlying the decisions the agency takes on drug approvals. A decade ago, when Guido Rasi took over as EMA director, significant relaxations and wider access were hailed as real progress, even by the—highly critical—EU Ombudsman. But the improvements are now being rowed back, says 

, the Paris-based publisher of independent drug reviews.

EMA’s transparency policy is “marred by too many failings,” it says in a lengthy analysis it published in May.

 It says secrecy has persisted and even become more pronounced in some areas, with new processes increasing response times with a queuing system, provision of documents staggered over months, and a delay to allow drug companies to institute legal proceedings when they disagree with the disclosure of their documents. Under the new processes, the identity of applicants for release of data can also now be disclosed to the pharmaceutical company that produced the document requested, 

 adds, suggesting that this risks undue pressure on its operations.

, when pharmaceutical companies have brought cases before the European courts to prevent EMA from releasing documents relating to marketing authorizations, EMA has refused to provide documents requested by 

, citing the existence of these legal proceedings as grounds for its refusal, “even when our requests were unrelated to the case.” The EMA’s intention in refusing to disclose these reports was to protect the commercial interests of the pharmaceutical companies that produced them, and to prevent the theoretical risk that a competitor could use the data as a basis for developing a similar drug. But the EU Ombudsman found the argument that disclosure of these clinical study reports could undermine the companies’ commercial interests to be unfounded, argues 

. The Ombudsman’s opinion led EMA to reform its transparency policy from 2011.

But EMA “struggled to meet its obligations on transparency,” and now the implementation of its policy of proactive disclosure appears to have stalled, too. The “major advance” achieved when EMA began spontaneously publishing large quantities of clinical data from marketing authorization applications in 2016 has been blunted by the opportunity given to companies to black out portions of these documents before their publication, including data from clinical study reports. On occasions, as much as 90% of the document has been so redacted. EMA has been particularly secretive toward 

, it alleges, systematically refusing to disclose major documents containing clinical data produced by pharma companies, as well major documents on adverse effects such as vital data in periodic safety update reports.

Hopes that EMA’s new transparency policy would provide wide access to clinical data from marketing authorizations proved over-optimistic, as disagreements between the agency and pharma companies over some or all of the reports disclosed have ended up before the European courts, and the disputes “have made EMA less transparent, at least in its dealings with 

’s case is built upon what it calls the right of European citizens to access clinical data related to marketing authorization applications. “This is a right that helps better protect patients,” it argues. The right of access to documents held by European institutions is a general principle in European Union law, it says. “Transparency is supposed to be the default position for European institutions, and non-disclosure of data the exception.” EMA is required to apply these principles, it insists.

The failings are of a scale that should justify an official inquiry to analyze the causes, according to 

, which expresses the hope that an inquiry would trigger renewed attention from the European Parliament and lead to appropriate remedies. What Prescrire wants to see is additional resources for EMA so it has no excuse for delays in meeting its transparency obligations. It also wants “strict oversight and close monitoring” of any redactions made to released documents. And it wants “fair consideration of applicants with no ties to industry, such as 

It blames the pervasive trend among drug regulatory agencies to withhold information on “a weak culture of transparency” and “fear of legal challenges by pharmaceutical companies.” The 2014 Clinical Trials Regulation, in force since the beginning of this year, included a number of advances in transparency, requiring EMA to publish clinical study reports once marketing authorization has been granted, via a publicly accessible database maintained and controlled by the agency. This went live in late January, “but we fear that the transparency requirements set out in this regulation will be undermined,” admits 

. The rules still allow pharma organizations to redact any data they consider commercially sensitive. “EMA must show that its policy is in line with the need to keep patients safe, rather than pretending to be transparent while allowing pharmaceutical companies to hide clinical data,” concludes 

https://english.prescrire.org/en/26A06BA9AEE5C0FDC137141EB68F92D2/Download.aspx

View from Brussels

Just-released analysis by Prescrire contends that the agency “is backtracking on commitments to clinical data transparency.”

Report Admonishes EMA Efforts to Reform Trial Transparency 

Today’s regulatory updates are driving innovation to revolutionize quality improvement in clinical research. While quality tolerance limits (QTLs) are standard in some industries, the International Conference on Harmonization’s ICH E6 (R2)

 represents the first time QTLs have been introduced for application in good clinical practice (GCP). Although early adopters have collaborated to understand and apply QTL-related practices, most clinical studies employing QTLs have not yet passed through regulatory inspection—leaving much room for discovery of and reflection on emerging best practices.

 along the journey to help stakeholders get ahead of the curve for QTL adoption. New insights will help our industry focus not on doing 

 to ensure impactful quality—without overburdening sites or sponsors. These quality improvements represent significant opportunities to reframe and rethink how we bring drugs to market, ultimately offering new hope to patients awaiting medical breakthroughs.

, an alliance of industry organizations through which sponsors, CROs and vendors collaborate in a progressive, pre-competitive environment. Our shared objective is to enable innovation, elevate quality and bring key stakeholders in the clinical trial process into greater alignment.

In late 2020, consortium members decided to develop guidance on the definition of QTL—regarding metrics, implementation and use—by channeling experiences from across the industry. A working group of 20 individuals (representing 16 organizations) met virtually during 2021 to discuss the challenges and develop a leading practice approach to be shared with the industry. The group adopted and built upon definitions of key terms used by TransCelerate Biopharma Inc. This series summarizes the areas of agreement that the working group considers most beneficial to the industry.

Traditionally, quality in clinical trials has been achieved by inspection—checking outputs for errors. Examples include checking data quality before database lock, auditing investigational sites and source document verification (SDV) by site monitors. While other industries have successfully adopted Six Sigma and lean process improvement methodologies—initially developed in the manufacturing industry during the second half of the 20th century—clinical research has been slow to adopt them.

These process improvement methodologies are based on two fundamental premises: designing quality into the protocol and processes from the start (a.k.a. quality by design (QbD)) and monitoring trial processes (rather than their outputs) to maintain them in a consistent state (in control). When a process is determined to be outside the normal operating parameters (out of control), the process is stopped; investigation takes place, and adjustments are made to put the process back on track. By focusing on the process, the intention is that an early signal can be detected—signaling a risk to the outputs. By responding to that signal, any output that does not meet requirements can be minimized and, hopefully, eliminated.

It is preferable not to produce faulty output at all, rather than produce it and then discard or rework it later. Detecting an early signal is critical to this approach. Process improvement efforts often initially focus on understanding the detailed cause and effect in processes to help establish the process measurements most likely to provide a signal that can be detected in time to act. This process focus has spread across many industries—particularly through the adoption of the ISO 9001 standard.

ICH E6 (R2) was adopted for clinical trials in 2016. Section 5.0 (Quality Management) owes much to the process focus described above. The “predefined QTLs” referred to are measurements that can give an early signal of risk to the overall quality—impacting human subject protection or reliability of trial results. The earlier a signal of risk can be detected, the more likely an investigation can lead to actions that minimize or even eliminate these quality impacts.

Consortium members have regularly discussed the challenges of implementing QTLs. One significant challenge is a limited understanding of “cause and effect” within clinical trial processes— in part because every trial is, by definition, new and different. ICH E6 (R2) requires that “important deviations from the predefined quality tolerance limits” are summarized in the clinical study report (CSR). There is, in effect, a balance of judgment taking place (see Figure 1 below). Ideally, an emerging issue is detected early to give enough time to act—to understand the issue and reduce its impact on the trial output. A parameter designed to provide this early signal will lead to signals due to non-systemic issues, i.e., noise.

For example, monitoring the proportion of trial participants who have withdrawn could lead to such a signal if one participant withdraws when only three are enrolled. There may be no evidence that the single withdrawal might impact other participants and, therefore, the trial output. In addition, newly defined parameters designed to detect emerging issues early might be imperfect, leading to further “false” signals.

If a QTL parameter is defined to provide an early signal of an emerging issue, such signals will each have to be assessed for whether they need to be reported in the CSR. Sponsors do not want to have to report deviations that are false signals because of a poorly defined measurement or because they are an artifact of limited data early in the study. As a result, the regulation has the effect of encouraging sponsors to define QTLs that are unlikely to be breached, which undermines the intended purpose of providing early detection of risk.

Another challenge is that the relationship between key risk indicators (KRIs) and QTLs is inconsistent across the industry. Many sponsors have adopted an approach of monitoring investigational site risk using KRIs due to the emergence of risk-based quality management methodologies. Deviations from KRIs do not have to be reported in the CSR, so KRIs can be designed to provide early signals of quality risk without the burden of including a summary of deviations in the final study report. However, the implementation and use of QTLs is often considered a separate exercise. This issue is compounded by the common misconception that any measurement of study-level risk must be a QTL rather than a KRI.

Consortium members participating in the QTL Working Group met over several months in 2021 to discuss these challenges and propose possible solutions. They agreed to accept the definitions of key terms from TransCelerate Biopharma

 and to build on that approach. The following proposals have been made:

KRIs can be defined and assessed at the study level and other levels (e.g., country, site, participant). QTLs can be considered the most critical study-level KRIs. This concept is described under 

Quality tolerance limits vs. key risk indicators

Determining the right QTL parameter and threshold for given risks is often difficult. Further guidance can help organizations with QTL definition, including areas where QTLs are typically used and 

The earliest possible detection of an emerging risk is important to managing study risks and reducing the likelihood of them becoming 

. The QTL Working Group proposes the use of companion KRIs to a QTL. These companion KRIs can provide an early signal of an emerging issue but do not lead to the burden of additional reporting in the CSR. The definition of KRIs can significantly impact the timeliness of risk detection. This concept is described in 

Let’s explore the relationship between QTLs and KRIs as applied to clinical research. KRIs are “metrics used to monitor identified risk exposures over time.”

 They have a metric definition and a threshold (or possibly two). When the value of the KRI goes beyond the threshold, there is a signal of an emerging risk or issue that may need investigating. They are most often used to detect site-level risks, and they trigger follow-up action steps such as a remote site contact or an on-site monitoring visit to investigate the risks and address any issues. Numerous site-level KRIs may be used in a study, with the total number typically ranging from 10–20. By detecting possible issues at a site early, actions can reduce the potential impact on data integrity or human subject protection.

While sites represent a primary focus of KRI-driven risk detection, the potential sources of risk on a given study may also include the geographic location (e.g., country) or individual participants. Many of the same KRIs used to monitor site risks may also help detect risks at these other levels. Similarly, KRIs can be defined and used at the study level. In this case, the data across all sites are aggregated, and the KRI is compared over time against a discrete risk threshold. Study-level KRIs are thus designed to detect risks and issues that are systemic in nature and not isolated to one or only a handful of sites in the study.

For example, the cycle time from participant visit to data entered into EDC (data entry timeliness) is a commonly used KRI. It detects sites taking significantly longer than expected to enter data, which may pose a risk to data quality. Additionally, it identifies an operational risk that the centralized data monitoring KRI dashboards are not based on near-real-time data. A study-level KRI for data entry timeliness may be useful to monitor the overall trend in the timeliness of data entry across all sites in the study. This could be compared against a standard “expected” time. Suppose the average timeliness across all sites (at study level) is higher than the standard. In that case, there is a risk for the study, which may warrant study-wide actions (e.g., awareness communication to all sites) vs. a site-by-site intervention.

QTLs as a special subset of study-level KRIs

As with KRIs, QTLs are a combination of the parameter (measurement) and threshold level (limit) used to help monitor risks to quality during the conduct of a study. QTLs can therefore be considered a subset of the study-level KRIs, designated to monitor the most important risks associated with critical-to-quality factors

 in the study. In other words, they are study-level KRIs that focus on study risks that have the greatest potential impact on data integrity or human subject protection. TransCelerate Biopharma recommends the use of 3-5 QTLs for a study. KRIs are not referenced in GCP—ICH E6 (R2), but QTLs are:

“Predefined quality tolerance limits should be established, taking into consideration the medical and statistical characteristics of the variables as well as the statistical design of the trial, to identify systematic issues that can impact subject safety or reliability of trial results. Detection of deviations from the predefined quality tolerance limits should trigger evaluation to determine if action is needed.”

Perhaps this concept has led to the perspective that QTLs are entirely different from KRIs. Table 1 below helps to highlight the differences and similarities between QTLs and KRIs.

According to ICH E6 (R2), QTLs should be “predefined,” which is taken to mean that the parameter and threshold should be established before study start or first participant in (FPI). The QTLs are indicators of whether the expected quality in the trial is being achieved, and, as with hypothesis testing, the expectation is set before gathering data. The aim is to avoid bias from data in the current trial when setting the QTL parameter and threshold. However, this does not mean that a QTL must remain unchanged throughout the course of a trial. Despite best efforts to establish QTL definitions and thresholds in advance, a study team may become aware of information not previously recognized or known that triggers some adjustments. It is important to clearly document the reason and justification for the updates in these cases.

There is an additional requirement in ICH E6 (R2) relating to QTLs:

“The sponsor should describe the quality management approach implemented in the trial and summarize important deviations from the predefined quality tolerance limits and remedial actions taken in the clinical study report (ICH E3, Section 9.6 Data Quality Assurance).”

Compared to KRIs, documentation requirements are significantly different for QTLs, referenced in ICH E6 (R2). The CSR should include an explanation of the selection of QTL parameters and QTL thresholds, a description of actions taken due to important deviations from the thresholds and a justification of any changes to the thresholds.

QTLs are a special subset of KRIs that focus on critical study risks.

Figure 2 below shows the relationship between QTLs and KRIs. The parameters used to monitor QTLs and KRIs are metrics. QTLs are a special subset of KRIs that focus on critical study risks.

There has been much discussion on interpreting the term “important deviation” in ICH E6 (R2). Most deviations from the QTLs are likely to be important, as they are the special subset of KRIs that focus on critical study risks. However, early in the study, where there are few participants, a QTL might be deviated from simply due to limited data. For example, one participant terminating early out of two enrolled may be simply a special cause for that one participant rather than a systemic study-wide issue. These deviations may not be considered “important” as long as the QTL is not exceeded at the end of the study.

 in this series which will highlight the process of defining QTLs. If you would like more information on the Consortium, please visit our 

, MBA (corresponding author), Senior Advisor, Metrics & Performance Management, WCG, Founder and former Executive Director, Metrics Champion Consortium

, Medidata (previously associated with PPD), 

Contributions by the co-authors and contributors are solely their own and are not intended to express the views of their organizations.

https://www.ich.org/page/efficacy-guidelines

https://www.iso.org/iso-9001-quality-management.html

Quality Tolerance Limits: Framework for Successful Implementation in Clinical Development. Bhagat, R et al. Therapeutic Innovation & Regulatory Science. 2020. 

https://doi.org/10.1007/s43441-020-00209-0

https://www.transceleratebiopharmainc.com/rbminteractiveguide/the-next-frontier-of-rbm/glossary-key-terms-defined/

https://www.ich.org/page/efficacy-guidelines#8-1

Series Part 1—Introduction and the relationship between QTL and KRI.

Defining Quality Tolerance Limits and Key Risk Indicators that Detect Risks in a Timely Manner: Reflections from Early Adopters on Emerging Best Practices (Part 1)

The COVID-19 pandemic has wreaked global havoc, with millions of lives lost. The effects of the pandemic on health care workers have been especially severe in terms of mortality, burnout, and diversion from non-pandemic critical work. This impact has also been felt in clinical research, just as the importance of clinical research has become obvious to the general public from the wide reporting of trials of drugs and vaccines for SARS-CoV-2 control. We became concerned that clinical research would be negatively impacted long-term, first by the interruption of the ability of research participants to be examined in person due to social distancing requirements, and then by the damage done by the pandemic to women clinical investigators, who play a vital role in clinical research. These issues were brought to the Clinical Research Forum, an organization formed to support academic clinical research, and the Board constituted an Academic Advancement Committee to consider the problem. One of the products of that committee was a Commentary recently published in 

 on the impact of the pandemic on women in clinical research.

The pandemic has had a disproportionate impact on women physicians and researchers, exacerbating long-standing issues of gender inequality in medicine. In the arena of clinical research,gender equity accelerates research excellence: we need multiple perspectives and all the brain power we can muster to maximize research productivity and quality. Moreover, women physician investigators enhance enrollment of women as participants in clinical trials, which is crucial to our ability to generalize from the data and to maintain the health of women. The upheaval wrought by the pandemic, which threatens the activity of women investigators in clinical research, also offers an opportunity to address the barriers faced by women in medicine.

For women researchers in the pandemic, rapidly shifting demands in clinical work, teaching, and mentoring hindered productivity at work. At home women have always borne a larger share of child care and domestic duties, with early career women physicians spending an average of 8.5 hours per week more than men,

 so women caregivers were most impacted by the abrupt closure of schools and child care facilities. Within months the disparities became evident in differences in publications submitted by women compared with men since the start of the pandemic.

Industry must act now to minimize effects of COVID-19 on clinical research.

Applied Clinical Trials-06-01-2022