Protocol Development #39

It's been a while since there's been a spotlight article - it's refreshing to see a roadmap that outlines how protocols will be developed, and used, in the future. Aside from this, we've got a bolus of patient engagement and DE&I themed articles, as well as resources covering process & templates, QbD, digital and structured content.

Spotlight

[Magazine] It's rare to come across an article that looks at the clinical study protocol without affiliation to a study design or therapeutic area and that intertwines multiple themes together; Todd Georgieff's article?on Navigating Toward a Digital Clinical Trial Protocol does exactly that. In the article, he looks at how multiple protocol-related themes are converging towards a digital protocol: regulatory influences (ICH M11), industry consensus (TransCelerate), protocol technology, and structured content management/authoring. In this case, what pushes the envelope is the consideration around not just looking at a digital protocol to serve once specific task, but rather serve as the origin for many processes (crucially not just for record-keeping but as a source of truth for the duration of the study). If you are looking for a roadmap for what is going to be coming up in the future for the humble protocol document, this is the article to read.??

Patient Engagement

[Article - Case Study] Boxall et al published Factors that influence women's enrolment and ongoing participation in a partially decentralised randomised controlled dermatology trial: a qualitative interview study with participants in the SAFA (Spironolactone for Adult Female Acne) trial. In the qualitative sub-study of the SAFA study (phase III study in adult female participants with facial acne vulgaris), participants shared their experience of recruitment to the study, the experience of appointments during the study (and/or televisits) and any difficulties in participation. From the 12 participants' feedback, 3 main themes were identified: (1) the influence of trust when deciding whether to enroll, (2) the feeling of validation from start to end and (3) offsetting participant burden with the understanding of trial needs. Overall the results were qualitative - and provide an interesting insight into patients' perspectives on the study.

[Article - Commentary] Lutz et al published Innovative Technologies in CNS Trials: Promises and Pitfalls for Recruitment, Retention, and Representativeness, a review that looks to address a situation in clinical research that the NIH and NSF conclude:

"current methods for conducting clinical trials are not sustainable and will leave a chasm between the need for evidence to inform health and healthcare and the availability of that evidence".

As representatives of the International Society for CNS Clinical Trials and Methodology (ISCTM), the authors comment on how such strategies, often lauded elsewhere, can offer promise yet deliver pitfalls. One such example is online recruitment (table above); despite the promise of greater geographic reach, greater representation, and lower cost - can it really deliver that for all target populations across all indications? Or will some studies be left with limited representation due to inadequate access to technology, low conversion rates, or data privacy concerns? There is one way to find out…engage with patients before finalizing your protocol.

[Blog post] Kelly Dumais and Stephen Raymond tackle What is important to patients in oncology clinical studies? Having spent the last weeks looking at a lot of different SoAs, I can reliably say that it is hard to tell from the protocol (and perhaps we should start being a little clearer on the issue as well?). In the post, the authors highlight how long it can take participants to complete PROs and what we can do (training and using a diary to enter symptoms) to support it. Given the preference for delivery method (figure above), a clear electronic PRO strategy appears to be the preferred approach for studies in an oncology setting.?

Diversity, Equity & Inclusion

[Article - Research] Smith et al published New Benchmarks on Demographic Disparities in Pivotal Trials Supporting FDA-Approved Drugs and Biologics, some good news (finally!) on progress in DE&I - there has been a significant increase in reporting of demographic data between 2007-21, and there has been a significant decrease in white participant overrepresentation, and an increase in Black representation. Regarding sex, racial data and ethnicity, each increased over the three time periods: 2007-11, 20112-16, 2017-2021.

[Conference proceeding] Maria Farooq reported on NCI-sponsored Cancer Clinical Trials Have Become More Diverse Over Past Two Decades, and reinforces the findings from Smith et al above. Although the details are limited in context and also restricted to US studies, the data points to a trend of wider enrollment. Given the somewhat contradictory - yet consistent - feedback on studies having greater eligibility restrictions, it would be interesting to see what factors contributed to this trend - was it deliberate (e.g., efforts to reach previously underrepresented populations) or was it by accident (e.g., due to evolving technology, better general oncology practice outside of specialist centers)?

[Article - Methodology] Coss et al published Does clinical research account for diversity in deploying digital health technologies? that tackles one of the key challenges with DHTs - does using DHTs enhance participation from underrepresented groups. Using social determinants of health (SDoH) scoring, the authors propose the used of 11 SDoH domains that align to five domains used by US ODPHP. From (what appears to be a secondary analysis from their existing evidence) an analysis of 126 publications, only 14.9% reported some SDoH scoring; even then - there were issues such as studies conflating terms for gender and sex.? The authors conclude that reporting on SDoH as standard would ensure equitable digital innovation.

[Article - Methodology] An et al published Promoting informed approaches in precision oncology and clinical trial participation for Black patients with cancer: Community-engaged development and pilot testing of a digital intervention. In the research, the authors present PINPOINT - a project designed to develop a digital educational tool to support decision-making for Black oncology patients. The purpose of which was to develop a scalable solution for empowering underrepresented Black oncology patients. With high acceptability and appropriateness rating, using a community advisory board to develop educational material to enhance education and decision-making appears to be a positive contributor to increasing DE&I access and knowledge transfer.

Process & Templates

[Article - Methodology] Ciani et al published A framework for the definition and interpretation of the use of surrogate endpoints in interventional trials where they provide guidance on how to define surrogate endpoints for protocols. I can personally attest to the frustration that some study teams have in defining endpoints that are both acceptable to regulatory authorities, and operationally feasible. The approach principally involves the justification for the surrogate endpoint that can be then used in a wider healthcare perspective (Fig.3). Importantly, the authors signal a need to move away from the myopic focus on biomarkers toward more inclusive intermediary endpoints that can incorporate measures of function and symptoms.

QbD

[News] Advarra published a post on New Clinical Trial Industry Survey Reveals Increased Burdens on Sites that reports on increased site burden of industry studies (for other recent articles see PD#38). The statistics are hardly surprising - 60% say study volume is higher, and 67% say that setup and training are now more burdensome. Budgeting and contracting were also show to be burdensome.

Digital

[Article - Methodology] Zhang et al published A Framework for Digital Data Quality Assessment in Digital Biomarker Research. Like Ciana et al above, defining the fundamental principles and the needs required to be fulfilled for successful use is a challenging task. The burden for using digital biomarkers (dBMs) is multifaceted, from the complexity of digital data (for a 50Hz sampling frequency, it's over 4 million data points per day), to full-spectrum quality and aggregation and reporting expectations. Although the article covers more detail in than what's needed in the protocol, the insight should help readers appreciate what goes into such decision-making - and what details are available. I, for example, would (at a minimum) want to know whether the dBM is mandatory or optional, what volume of data is collected (per patient and anticipated percentage completeness), the time period, and what kind of aggregate reporting would be provided per participant. As per the SPIRIT guidelines:

"Primary, secondary, and other outcomes, including the specific measurement variable (e.g., systolic blood pressure), analysis metric (e.g., change from baseline, final value, time to event), method of aggregation (e.g., median, proportion), and time point for each outcome. Explanation of the clinical relevance of chosen efficacy and harm outcomes is strongly recommended."

[Article - Research] Sato et al published Implementation status and consideration for the globalisation of decentralised clinical trials: a cross-sectional analysis of clinical trial databases. The authors looked at DCTs in the USA and Japan, and found a gradual increase from 2001 up until a significant increase in 2020 as the COVID-19 pandemic put significant restrictions on the global population. If there's 2 things to look at, it’s the overall figure above and Table 1 that categorizes DCT components by therapeutic area; what is interesting in Table 1 is the distribution between the 4 DCT component categories, a clear reflection of shifting priorities for different therapeutic areas.

Structured Content

[Blog post] Val Swisher published Leapfrogging Structured Content in the Move to Generative AI that scrutinizes a question that is on a lot of peoples' minds: can we skip structured content and move to generative AI? For those who are on the SCA path the good news is "no" - you can't. Not only does effective training of a LLM require good data in, the possibility of "hallucinating" increases whenever data is inconsistent and contradictory. Much in the same way that jumping the gun on protocol development when your study design is immature, it's not advisable - continue cutting corners at your own peril.