12/22/2025  |  Andrew Mackinnon  |  BioXconomy

https://www.bioxconomy.com/clinical-and-research/how-ai-agents-are-transforming-the-clinical-trial-process-for-cras 

Agentic AI emerges as solution to clinical trial inefficiencies, offering CRAs relief from manual tasks while maintaining human oversight for patient safety.

Clinical trials have become increasingly mired in operational bottlenecks and administrative inefficiencies over the years, delaying approvals of new and potentially lifesaving therapies. Between 2020 and 2024, while the time between individual clinical trials decreased by an average of seven months, the average clinical trial cycle times (defined as the total duration from the approval of a clinical trial protocol to the database lock) increased by 14 months. Given the decades long trial process already, we cannot afford even more time to further anchor today’s clinical trials. 

Of course, trial inefficiency has an indirect-yet-consequential impact on patient outcomes. However, it also infringes on the daily workday of clinical research associates (CRAs) who are on the frontline of trial execution and experience first-hand the implications of a growing list of bottlenecks. Many CRAs are overwhelmed by the ever-growing burden of manual tasks, number of systems to monitor, and volume of data generated by a trial, or otherwise ensnared in clinical trial “white space” — i.e., extended periods of inactivity that can stretch on for months and hinder progress both between and throughout trial phases. Inefficiencies are felt across study teams yet CRAs feel the full weight of this burden daily.

CRAs’ peak frustration is arriving, somewhat fatefully, alongside critical breakthroughs in agentic artificial intelligence (AI) and automation. Even in their earliest stages, these technologies have the potential to mitigate white space, accelerate new therapy approvals, and redefine the entire clinical trial process and role of CRAs in this era of innovation. Ultimately, as AI matures feverishly, agents will better support ongoing advancements in modern medicine already being seen upstream in drug discovery.

The growing burden of inefficiency in clinical trials
Clinical trials are conducted via inherently complex, multi-phased processes, all of which must be navigated with incredible care to ensure alignment with the protocol and strict regulatory standards. From one view, this is exactly how it should be; carelessness and efficiency for efficiency’s sake frankly have no business in the field of clinical research. Yet, most of the operational bottlenecks thwarting CRAs today are not necessary. Rather, in most cases they are emerging from the rapidly deteriorating ability of traditional administrative systems to support the evolving complexity of clinical trials.

For perspective, IQVIAreports that white space currently consumes close to half of a new drug’s development time. However, due to the study’s limited definition of the term, that already startling statistic does not even account for the myriad productivity gaps that occur within every clinical trial conducted across an individual drug’s development lifecycle.

Tufts Center for the Study of Drug Development (CSDD) attributes increasing trial complexity to an upward trend impacting ALL protocol design variables, especially related to Phase II and Phase III trials. From site selection and activation to endpoints and patient eligibility criteria, trials are more complicated today. And the sheer volume of data being produced and disseminated across multiple systems throughout each trial is significantly greater than ever before — a 2025 Tuftsstudy found that Phase III trials each average nearly 6 million data points, and the number of data points per trial has roughly tripled over the past decade!

CRAs are responsible for monitoring such mega-complex trials, supporting sites in the operational conduct of the protocol while ensuring data integrity and patient safety at every step. They must accurately monitor data from dozens of separate systems to ensure they can build an accurate picture of study status and identify areas of need plus the next best action to take. Instead of focusing on guiding and supporting sites and adding strategic value, CRAs are chasing missing data and are reactively tactical.

Take it from career CRAs, who recently shared their professional experience confronting these inefficiencies in the clinical trial process:

“I have three site selection visits in a week,” said Shay, a CRA with eight years of experience. “I have to do a report for each one of those that are up to 100 questions each per site and the draft is due within five days.”

“If you are assigned 15 sites across four different protocols, it is very hard to keep track of everything,” said another CRA who has been in her position for six years. “Plus, things change in real time. For example, I’ll tell a site the 10 tasks they need to complete for each of their 15 employees, and two days later they’ll come back and say they did those things, and I’ll have to go back into each system to annotate this across all my assigned sites. It is very administrative. 

Another CRA said, “When I was reconciling, I was completely exhausted because I must check all of the data — that this patient is consenting on this and so many other variables. It was about three months of tasks.”

More than a chatbot: Closing productivity gaps with agentic AI
Highly capable and experienced CRAs constantly battle against time, with increasing volumes of white space slowing clinical trials. Moreover, it highlights the futility of increasing manual administrative processes in clinical research. Human CRAs will never be able to keep pace with expanding complexity and quantity of manual tasks without an automated solution. Worse still, CRAs are unable to proactively address site issues due to the murky picture of site status spread across multiple clinical systems. 

This is where agentic AI has the potential to be transformative.

Unlike purely predictive generative large language models and chatbots, AI agents can become virtual teammates, working semi-autonomously and systematically toward a set of specific goals rather than merely responding to isolated inquiries. Additionally, the AI agents being developed for CRA support today are not trained on troves of generic, disconnected data but on deep, vertical-specific expertise and grounded in regulatory guidelines, SOPs, and study plans, ensuring their actions are reliable, ethical, and informed by the nuanced regulatory and scientific realities of clinical research.

At the most basic level, AI agents can be used to automate everything from routine administrative tasks to complete clinical trial workflows, which on their own is enough to generate significant time savings and free up CRAs to focus on more important, human-centric responsibilities. But the real benefit is agents’ ability to act as a strategic coordinator with an unlimited capacity to learn and improve their performance based on past experiences.

For example, the traditional, linear approach to site activation is a notoriously long, step-by-step process that tends to yield considerable delays and white space due to frequent disconnection and miscommunication between multiple stakeholders. By the time all tasks are completed and the data from all systems has been painstakingly collected and processed — sometimes over months — teams have had very little constructive learnings about the process … except that too many independent variables exist to conceive of a more effective strategy.

Contrast this with an AI agent that can tackle most of the daily administrative burden. Agents work with the standard knowledge of what is needed for each country, site, and Institutional Review Board (IRB)/Ethics Committee (EC) and automating the collection, review, and compilation of document packages. Now, CRAs are free to focus on strategic activities like helping sites operationalize the protocol so that less time is wasted when stakeholders are working at different paces. When complete, the agent can then analyze all relevant operational data to identify patterns and potential areas for improvement, ultimately helping to tailor future strategies based on the most effective approach for different site types, regions, therapies, and levels of protocol complexity.

Perhaps the most attractive benefit of agentic AI in clinical research — at least from the perspective of an overworked and overwhelmed CRA — is the ability to automate and streamline site monitoring and data management processes. By consolidating complex trial data across dozens of systems, monitoring hundreds of trial variables simultaneously, and suggesting smart real-time next-best actions, agents give CRAs the tactical support they desperately need while enabling proactive intervention before small issues become major problems, allowing CRAs to return to their core role of supporting sites.

Balanced with appropriate checks and humans-in-the-loop, AI agents optimize CRAs’ time so they can focus on the most important activities like monitoring patient safety and working with sites.

A crucial partner, not a replacement
While the development and ongoing refinement of agentic AI has massive implications for the efficiency and quality of clinical trials, the primary aim of this technology is to support and not to replace human CRA oversight and expertise. Yes, the level of autonomy given to AI agents is very likely to increase over time alongside their capabilities, but it will be the autonomy of a complimentary digital co-pilot with the goal of relieving highly skilled CRAs of more tedious work so they can focus their time where the human touch is crucial to advance the trial, support sites, and care for patients.

This clinical trial transformation has never been more urgent or more vital, especially considering the broader use of AI further upstream in drug development. As AI-driven drug discovery identifies more potential drug candidates faster with ever-increasing chances of success — thanks to continuously improving computational biology — the creaking and overburdened clinical trial funnel will become the single greatest obstacle to treatments reaching patients. The boundaries of science will melt away and only process mechanics will block human healing. As Novartis’ Vice president/head of digital innovation and AI in drug development, Max Lawson, put it at Novartis AI and Digital Day: “Accelerating new medicines is arguably the most important way AI will benefit humanity.”

Time is life. With AI agents as partners across the clinical trial lifecycle, the industry can match the speed of AI-powered drug discovery in drug development. Fully leveraging AI technologies across the entire development paradigm will result in the most monumental wave of new human medicine ever seen. Let’s go.

12/19/2025 | Butantan Institute

https://butantan.gov.br/noticias/instituto-butantan-dara-inicio-a-entrega-de-13-milhao-de-doses-da-vacina-contra-a-dengue-ao-ministerio-da-saude 

The first 300 thousand doses will be delivered at the beginning of next week; Until the end of January 2026, more than a thousand doses will be available to the National Immunization Program (PNI)

The Butantan Institute, an organization linked to the Secretary of State for Health of São Paulo, will begin next week with the delivery of 1.3 million doses of the vaccine against dengue for the National Imunization Program (PNI) of the Ministry of Health. The first 300 thousand doses will be delivered starting next week and, until the end of January 2026, more than a thousand doses will be delivered. Butantan-DV is the first vaccine in the world to be used alone against dengue. The confirmation of the contract was carried out on this sixth fair (19) between the Butantan Institute and the Ministry of Health.

Delivery is being possible because, even before approval by the National Sanitary Surveillance Agency (Anvisa), the Butantan Institute had already begun, above all, the production of the immunizer in its industrial park. Além disso, Butantan formed an international partnership with the Chinese company WuXi to increase production and expand delivery in the second half of 2026.

"More an important milestone in this day of achievements of Brazilian science. With the formalization of the agreement between the Butantan Institute and the Ministry of Health, we will begin the delivery of two doses of the vaccine against dengue. As well, in January, it will be possible to begin placing the vaccine in the arms of two Brazilians," says the director of the Butantan Institute, Esper Kallas.

Butantan-DV was approved by Anvisa to be used by the Brazilian population from 12 to 59 years old. Public use, the immunizer showed 74.7% general effectiveness, 91.6% effectiveness against severe dengue and with alarm signals and 100% effectiveness against dengue hospitalizations.

In accordance with the strategy announced by the Ministry of Health, these first two deliveries at this time by the Butantan Institute will be destined for the Primary Care professionals, who operate in the Basic Health Units (UBSs) and in home visits. With the expansion of productive capacity, the ministry should extend vaccination to the general public, starting with adults aged 59 and gradually advancing until reaching the age of 15.

Other publics
The Butantan Institute aims to expand the age range of the vaccine both for the pediatric public and for those over 60 years of age. For this reason, you have received approval from Anvisa to validate the dengue vaccine in the population between 60 and 79 years old.

If the results of the investigation are not satisfactory, it will be possible to request the regulatory agency even from this group for the immunization recommendations. Also, more data should be collected to validate the possibilities even of children from 2 to 11 years old in the recommendations of the cow. The clinical studies carried out have proven that the vaccine is safe in this age group.

12/22/2025  |  Bree Foster  |  DDN/Drug Discovery News

https://www.drugdiscoverynews.com/can-we-stop-using-mice-for-research-16888 

As nearly 90 percent of drugs fail in human trials, researchers are turning to patient-derived cells, engineered tissues, and AI to better predict human responses and reduce costly late-stage failures.

For more than a century, animal experiments have been central to life sciences, facilitating foundational discoveries such as the germ theory of disease and advancing our understanding of conditions like tuberculosis, polio, and rabies. Today, most modern drugs, including penicillin, cancer drugs, and HIV (human immunodeficiency virus) treatments, have first been validated in mice before entering clinical trials and eventually saving lives.

However, while animal studies have yielded valuable insights, their relevance to human disease is limited by profound species differences. These differences mean that as many as 92 percent of drugs that appear promising preclinically ultimately fail in human trials. Many of these, approximately 75 percent, fail due to a lack of efficacy or safety, showing just how poorly animal data often translates to real patients.

Researchers are increasingly adopting human-first approaches that more accurately model human biology. Techniques such as organoids, human induced pluripotent stem cells (iPSCs), and engineered tissues or microphysiological systems, such as organ-on-a-chip platforms, allow scientists to study tissue development, disease mechanisms, and drug responses in a human-specific context. This approach generates insights that are more likely to translate from the bench to clinical outcomes, accelerating the development of safe and effective therapies.

Reflecting this scientific momentum, regulatory frameworks are beginning to shift as well. In late 2022, President Biden signed the FDA Modernization Act 2.0 into law, removing the legal requirement that drugs be tested in animals before entering clinical trials. And in 2025, the agency began phasing out animal testing for monoclonal antibodies and other drugs, encouraging instead the use of human cell-based platforms and simulations using artificial intelligence (AI) and modelling.

Lorna Ewart, Chief Scientific Officer at Emulate, called this a tipping point. “The FDA Modernization Act 2.0 and the National Institutes of Health’s (NIH) commitment to prioritize funding of New Approach Methodologies (NAMs) over animal-exclusive proposals signal a fundamental shift from ‘permission’ to ‘expectation’ that human-relevant models will play a central role in drug development. Regulators across the globe are not only opening the door to these technologies — they are actively encouraging sponsors to use them.”

To understand why this transition matters, it helps to revisit how mice became so entrenched in the first place.

Why rodents have been the default
For more than a century, mice have been the silent workhorses of biomedical research. Their short lifespans, rapid reproduction, and ease of breeding make them convenient and cost-effective, while their small size and adaptability have allowed labs across the world to house and study them at scale. Crucially, humans and mice also share a very similar genetic background, with around 90 percent of their genomes arranged in conserved regions. Over time, this combination of practical and biological advantages has made the common house mouse the default model for testing everything from vaccines to cancer therapies.

The widespread use of mice has also driven the development of extensive research resources, including thousands of genetically defined inbred strains, a complete reference genome, deep sequencing data for dozens of inbred lines, and detailed maps of genetic variation. Advanced tools for precise genome manipulation allow scientists to replicate human conditions in mice with remarkable fidelity, creating models that are central to preclinical research.

However, despite these advantages, mice are not miniature humans. The most obvious difference is size: humans are thousands of times larger than mice, and this affects metabolism, life expectancy, reproduction, and diet. There are also differences in circadian rhythm, cognitive development, sensory systems, and social behavior. Physiological differences extend to organs and tissues as well. Mice differ from humans in relative organ size, structure, regenerative capacity, disease susceptibility, cell duplication time, immune response, and drug transport.

Moreover, the domestication and breeding of laboratory mouse strains have increased these inter-species differences. Inbred laboratory strains lack the genetic diversity seen in human populations, masking variability in drug responses and disease progression. Environmental exposures, diet, and lifestyle, all of which are crucial contributors to human disease, are also absent in standard mouse models.

As Thomas Hartung, Director of the Center for Alternatives to Animal Testing at Johns Hopkins University, told DDN, “Efficacy translation is scarcely better than a coin flip across indications, and even safety signals are missed or misprioritized due to species differences, young and healthy inbred strains, artificial disease induction, small cohorts, and short study duration. This contributes to about 90 percent clinical attrition and late safety surprises.”

These disparities can have profound effects on disease modeling. For example, many anticancer therapies show dramatic success in mice but researchers fail to replicate these results in humans. Given these differences, it is perhaps unsurprising that disease patterns and drug responses in mice often do not reflect what happens in people.

Joseph Wu, Director of the Stanford Cardiovascular Institute and co-founder of Greenstone Biosciences, emphasized that these limitations are driving a shift toward human-relevant models. “Mice have been invaluable for uncovering basic biology, but their predictive power for human drug responses is limited. Cardiovascular physiology differs across species; mice have heart rates five to 10 times faster than humans, distinct ion channel properties, and different metabolism.”

“Mice remain useful for systemic interactions and whole-body physiology,” he said. “But human iPSCs, organoids, and engineered tissues allow us to capture patient-specific genetics and disease phenotypes in ways that mice simply cannot.”

Ewart echoed this sentiment, “Human-based systems such as iPSCs, organoids, and organ chips not only address the species gap by providing data that is directly human-relevant, but they also deliver results faster and at lower cost. This combination of accuracy, speed, and efficiency is what makes them so powerful as alternatives to traditional animal testing.”

The rise of human-based models
One of the most widely used human-based models is iPSCs. First developed in 2006, iPSCs are generated by reprogramming adult somatic cells, such as skin or blood cells, back into a pluripotent state, giving them the ability to differentiate into virtually any cell type. This technology allows researchers to model human tissues and diseases in a way that more reliably mirrors human physiology, disease characteristics, and pharmacogenomics.

At Stanford University, Wu’s lab has built one of the world’s largest iPSC biobanks, now housing over 2,500 lines derived from individuals spanning diverse ages, sexes, and genetic backgrounds. “Traditional mouse models will not recapitulate this genetic variability,” Wu explained. “With large representation of patient-derived iPSCs, we can capture that variability directly in the lab, which helps explain why one patient may respond well to a drug while another develops toxicity.”
iPSC-derived models have opened the door to what Wu calls “clinical trials in a dish.” By testing hundreds of patient-specific cell lines in parallel, his team can identify responders and non-responders before moving into human trials, uncover genetic drivers of drug response, and better predict adverse effects.

Beyond disease modeling, iPSC platforms can be paired with advanced computational tools. Wu highlighted the ADMET-AI platform which “leverages large clinical toxicology datasets. Unlike animal models, it can rapidly forecast human-specific cardiotoxicity risks across thousands of compounds, something preclinical testing in mice simply cannot capture at that scale or with patient relevance. “

Rethinking the role of mice in research
Cardiac fibrosis is a scarring of the heart muscle due to excessive collagen deposition, which impedes heart function, causes symptoms like shortness of breath and swelling, and can lead to heart failure. Unfortunately, no therapies have been approved for this condition. Most screenings for antifibrotic therapies have relied on mouse fibroblasts; however, these models are limited by considerable species differences, the lack of counter-screening for cardiotoxicity, and the inability to replicate contractile function.

Instead, Wu’s team turned to human iPSC-derived cardiac fibroblasts (CFs), patient-specific cells capable of faithfully modeling human disease in vitro. Using a high-throughput screening platform, the researchers tested roughly 5,000 compounds across multiple doses and independent iPSC lines, while counter-screening in iPSC-derived cardiomyocytes and endothelial cells to exclude compounds with potential cardiotoxicity.

Artesunate, a powerful antimalarial drug, emerged as the top hit. In human fibroblasts, it reduced proliferation, migration, and contraction, and decreased collagen deposition. To strengthen translational confidence, the team employed a layered, multiscale approach: engineered heart tissues recreated the 3D cellular and mechanical environment, revealing how fibroblasts and cardiomyocytes interact; mice provided systemic insights into drug metabolism, distribution, and organ-level effects; and computational simulations allowed target identification and drug–protein docking, pinpointing artesunate’s interaction with MD2 (myeloid differentiation factor 2).

This multiscale approach is “absolutely critical because no single model can capture the full complexity of human disease. This multiscale validation gave us the confidence to advance artesunate into early-phase clinical trials for pulmonary arterial hypertension and idiopathic pulmonary fibrosis. More broadly, this layered strategy reflects what regulators are now encouraging under the FDA Modernization Act 2.0 and new NIH NAMs guidance, shifting from reliance on single animal models toward integrated, human-first approaches that combine cellular, tissue, animal, and computational evidence. I believe it’ll become a cost-effective way to reduce late-stage failures and deliver therapies that are truly translatable to patients,” said Wu.

For Wu, these cases highlight a new balance in biomedical research. “Human models give us precision, animal models provide context, and together they make drug development more efficient,” he says. Mice still play an important role, but increasingly as part of the supporting cast in a research pipeline that now begins with human-relevant models.

From cells to miniature organs
Advances in stem cell culture have made it possible to generate large, uniform populations of specific human cells. These can be assembled into engineered tissues or organ-on-a-chip systems, enabling scientists to study organ-level physiology and disease in ways that were once impossible outside the human body.

“Mouse models have taught us a lot about organ formation,” Wu notes, “but there are limits to what we can learn about human development in vivo. Ethical restrictions and species differences mean that the earliest stages of organogenesis remain largely uncharted in humans.”

Using spatially micropatterned iPSCs and four fluorescent reporter systems, Wu’s lab has guided cells to self-organize into cardiac and hepatic organoids with lumenized, branched vasculature. These organoids, or “gastruloids,” mimic key aspects of human embryonic development during the first three weeks post-conception, revealing how the heart and liver begin to form their intricate structures.

A carefully optimized cocktail of growth factors and small molecules guided the formation of branched, lumenized vasculature within organoids containing multiple interacting cell types, including endocardial, myocardial, epicardial, and neuronal cells. These organoids reproduce key structural and functional features of early human organs, allowing researchers to study vascularization, cell-cell interactions, and developmental signaling in a human-specific context.

However, these models are not perfect. “Current gastruloids lack full maturation, long-range vascular perfusion, functional innervation, and the dynamic immune-stromal interactions that shape organ development and disease. The next frontier is to integrate hierarchical vasculature, neural input, and multicellular niches to move them closer to physiological fidelity,” noted Wu.

Still, they offer a human-specific lens that no mouse model can provide, allowing researchers to study early development, model diseases, and test drugs in ways that were previously impossible. “The challenge now is systematically closing these gaps so that they can serve not only as discovery platforms but also as credible tools in the drug development pipeline,” said Wu.

Limits of current human models
Even as iPSCs, organoids, and gastruloids reshape drug discovery, the path is far from straightforward. Hartung cautioned that scientific and practical hurdles remain: “We still need broader qualification of human models, quantitative links to clinical endpoints, and consensus pathway mapping for toxicities. On the practical side, standardization, cost-of-entry, workforce training, and regulatory familiarity remain uneven.”

One major hurdle is scalability. “Regulators and sponsors need large, reproducible datasets before they can replace entrenched animal models. Until recently, the throughput of NAM technologies was simply too low to generate that level of evidence. But with new platforms capable of supporting dozens to hundreds of replicates per run, that barrier is coming down,” said Ewart.

Generating patient-specific iPSCs, differentiating them into specialized cell types, and maintaining large biobanks demands time, space, and resources. While pooling hundreds of lines can increase throughput, differing growth rates and functional readouts can complicate results, sometimes masking patient-specific responses. Recent advances in single-cell technologies are helping to overcome this. By barcoding each line through whole-genome sequencing, researchers can track individual cells in pooled experiments and link gene expression data back to the original donor. This approach allows high-throughput analysis across diverse genetic backgrounds, bringing large-scale preclinical testing in human iPSCs closer to reality.

Reproducibility is another challenge. Differentiation protocols can yield subtle variations across lines or labs, affecting disease modeling and drug testing outcomes. Standardization is improving, but large-scale, multi-site studies are still limited by variability in cell quality and maturation.

Finally, regulatory integration remains a moving target. The FDA Modernization Act 2.0 and NIH NAMs guidance support human-first evidence, but translating that data into clinical approvals still demands strategic validation, likely using a combination of iPSCs with engineered tissues, computational models, and selective animal studies.

So, can we stop using mice for research? The answer may be yes — but not completely, at least not yet. Instead, their role is evolving from central to supportive, as human-first platforms take the lead in predicting drug responses, understanding disease, and ultimately bringing safer, more effective therapies to patients.

12/22/2025  |  Suzanne J. Rose  |  Clinical Leader

https://www.clinicalleader.com/doc/pillars-to-support-community-hospitals-first-steps-into-clinical-trials-0001 

Community hospitals are often seen as centers for clinical care delivery and not as sites for research activities. Clinical trials need not only be conducted in academic medical centers; they can be strategic assets that elevate reputation, attract talent, strengthen community trust, and generate new revenue streams for community hospitals as well.

For leaders at institutions like Stamford Health, the challenge with embarking on clinical research can lie in justifying the value of clinical trials to the C-suite. Executives must weigh investments against competing priorities, and research programs can be misunderstood as costly or peripheral. However, when properly supported, clinical trials deliver measurable returns — financial, operational, and reputational.

This article outlines six pillars community hospitals can leverage to make a compelling case for clinical trials: dedicated infrastructure, community engagement, technology platforms, financial support, strategic partnerships, and physician engagement.

1. Dedicated Research Infrastructure
For the C-suite, infrastructure is not an expense but an investment. It enables faster trial activation, reduces compliance risks, and positions the hospital to engage with our community while attracting industry-sponsored studies. Building a strong and diverse team is only made possible by allocating funds specifically for research in the fiscal budget, which demonstrates institutional commitment.

To that end, Stamford Health established a research team1 that consists of dedicated clinical research coordinators, regulatory specialists, data managers, a research pharmacist, and a research financial analyst to ensure compliance and efficiency. The staff is centralized to create visibility and streamline operations but is mobile enough to support principal investigators and patient care at various off-site locations.

2. Community Engagement
Additionally, research demonstrates that equitable representation improves trial outcomes and patient trust.2,3 Therefore, we knew recruitment strategies must reflect the city of Stamford’s diverse population.4 National recommendations emphasize that adequate representation of women and underrepresented racial/ethnic groups is both an ethical and scientific imperative.5 To reach our community, we utilize educational programs that build trust and dispel myths about research using our Community Healthcare Worker Program.6 Another powerful method is to utilize patient stories through sharing testimonials that demonstrate tangible impact through our various social media platforms. For the executive team, community engagement strengthens our reputation and fulfills our mission to support the community in which we serve.

3. Technology Platforms
Modern research requires modern tools. Two essential tools worth their initial investment are a clinical trials management system (CTM) and an electronic regulatory binder solution:

• CTMS: Implementing a CTMS is a critical move for revenue integrity and risk mitigation, which has effectively transformed our research operations from a manual liability into a disciplined revenue center. Previously, reliance on spreadsheets exposed the hospital to financial leakage through missed invoiceable items and created federal compliance risks regarding Medicare double-billing. Along with a research billing compliance program,7 the CTMS helps ensure we capture every dollar owed while safeguarding against costly audit penalties. Because the subscription costs are offset by increasing administrative fees charged to sponsors, along with obtaining monies earned, the system effectively pays for itself while securing the hospital’s bottom line.
• eRegulatory solution: Implementing eRegulatory is a strategic necessity that transitions the hospital from a reactive, paper-heavy liability to a proactive, audit-ready research hub. By digitizing regulatory binders, the hospital eliminates the physical storage costs and administrative burden of manual filing, directly empowering coordinators to redirect time back to patient recruitment and care. eRegulatory systems enable remote monitoring capabilities, which can reduce on-site monitor visits and their associated operational disruptions, while simultaneously positioning the hospital as a preferred site for sponsors who prioritize rapid, remote-accessible data. The ROI is realized not just in hard cost savings on paper and archival fees but in accelerated study start-up times — often cutting weeks off the site activation phase — allowing the hospital to open trials faster and capture enrollment revenue sooner than manual competitors.

Executives should view these platforms as risk-reduction tools. They minimize errors, enhance efficiency, and improve throughput — ultimately increasing competitiveness in securing trials.

4. Financial Support
Clinical trials often require up-front investment before external funding begins to flow. A program does not have to start with a large number of trials or participants, but it must invest in highly interested and qualified principal investigators and create centers of excellence from which research programs can grow. One or two trials with strong recruitment and budgeting can quickly demonstrate the value of one clinical research coordinator and one principal investigator. By growing the program on high-producing PIs, a site can recruit other investigators and mentees of established PIs. When a growing trial portfolio demands additional staff, a clinical research workload staffing tool is of great value.8 In addition, private funders and research-oriented foundations are a possible avenue for philanthropic and grant support.

5. Strategic Partnerships
Stamford Health’s centralized Department of Research and Discovery provides a broad range of high-quality studies, including National Cancer Institute trials and pharmaceutical-sponsored protocols.1 Our partnership with Dana-Farber Cancer Institute provides our cancer patients access to cutting-edge, complex clinical research trials while receiving care in the community hospital they know and trust. This directly fulfills the community hospital mission of providing equitable, high-quality care close to home.

Our diverse research portfolio is critical to ensure the department is both scientifically relevant and financially sustainable within the Connecticut healthcare landscape. Pharmaceutical-based research guarantees a steady, diversified revenue stream for the department, as industry-sponsored trials are typically fully funded and offer competitive overhead. By combining prestigious academic access for reputation and advanced care with pharmaceutical funding for financial stability, Stamford enhances recruitment efficiency, attracts top clinical staff and physicians, and solidifies its standing as a leading regional provider. For the C-suite, partnerships mitigate risk and amplify impact by allowing community hospitals to offer patients access to therapies typically reserved for large academic centers.

6. Physician Engagement Initiatives
The PI serves as the indispensable leader in every clinical trial, responsible for the study's execution, regulatory compliance, and most critically, the participants’ welfare. However, establishing fair and compliant compensation while ensuring physicians are engaged in research remains a persistent challenge for research sites at community hospitals. Securing competitive compensation is vital to attract, retain, and motivate the highly qualified investigators necessary to drive clinical research forward.

As most physicians at community hospitals and systems are compensated on a relative value units (RVU)-based model, it is not always possible to allocate protected time for PIs with a demanding clinical schedule. To combat the inherent tug of war between work RVUs and research, a site can create research RVUs (rRVUs) for clinical research-related activities with billable Current Procedural Terminology (CPT) codes. Hospitals can then integrate clinical research into their physician management and compensation systems. Investigators earn a set number of rRVUs for clinical research activities, ensuring clinical research is not viewed as competing with standard of care visits.9

Along with the rRVU, fixed fees can be utilized for costs that are consistent from study to study, such as site initiation or monitoring visits. To accommodate assessments in the clinical trial that are only research-related without corresponding CPT codes, a fee-for-service model can be utilized for study procedures because they would be variable from visit to visit and study to study. This system is consistent with the study’s financial success while remaining within regulatory guidelines. 9

Our physicians are powerful messengers and influencers in community health initiatives, and their engagement accelerates progress. From a leadership perspective, physician engagement improves recruitment, retention, and morale.

The Future Of Clinical Trials Includes Community Hospitals
Clinical trials are not cost centers; they are strategic investments. For community hospitals like Stamford Health, the value lies in infrastructure, technology, financial support, partnerships, physician engagement, and community outreach.

By framing clinical trials as vehicles for innovation, reputation, and sustainability, hospital leaders can move beyond skepticism and embrace research as a core part of their mission. The message to the C-suite is clear: Clinical trials are essential to the future of community healthcare.

References:

1. Department of Research and Discovery. Stamford Health. https://www.stamfordhealth.org/care-treatment/research/
2. Reopell L, Nolan TS, Gray DM, et al. Community engagement and clinical trial diversity: Navigating barriers and co-designing solutions—A report from the “Health Equity through Diversity” seminar series. Hood K, ed. PLOS ONE. 2023;18(2):e0281940. doi:10.1371/journal.pone.0281940
3. Hosely M. Diversity, Equity, and Inclusion in Clinical Research. Advarra. September 19, 2024. Accessed December 7, 2025. https://www.advarra.com/blog/diversity-equity-and-inclusion-in-clinical-research/
4. Stamford, CT | Data USA. Accessed December 7, 2025. https://datausa.io/profile/geo/stamford-ct#:~:text=The%205%20largest%20ethnic%20groups,%2DHispanic)%20(7.03%25).
5. Corneli A, Hallinan Z, Hamre G, et al. The Clinical Trials Transformation Initiative: Methodology supporting the mission. Clin Trials. 2018;15(1_suppl):13-18. doi:10.1177/1740774518755054
6. Stamford Health implements community health worker program to increase health care access. Stamford Health. January 11, 2024. Accessed December 8, 2025. https://www.stamfordhealth.org/healthflash-blog/stamford-health-staff/stamford-health-implements-community-health-worker-program/
7. Rose, Suzanne J. Maximizing Clinical Research Billing Compliance: One Site’s Journey to Success. Compliance Today Mag. Published online April 2024. https://bit.ly/4aelOjc
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12/19/2025  |  RFI/Terra.com

https://www.terra.com.br/noticias/mundo/estados-unidos/eua-trump-propoe-reclassificar-maconha-para-categoria-menos-perigosa-e-facilita-pesquisas-medicas,4ff0aec7ca8a32b740e6ad38ef953b6a7sjoe4r9.html

The measure, described by the White House as a “common sense” step, represents the most significant change in federal policy on cannabis

On Thursday (18), US President Donald Trump signed an executive order reclassifying marijuana at the federal level, removing it from the category of drugs considered most dangerous and without recognized medical use. The measure, described by the White House as a “common sense” step, represents the most significant change in federal policy on cannabis since 1970, when the substance was included in the list known as Schedule I, alongside heroin, LSD, and ecstasy.

According to the decree, marijuana should be moved to Schedule III, a category that includes substances with “moderate to low potential for dependence,” such as medications containing codeine. The executive order instructs Attorney General Pam Bondi to expedite the reclassification process with the Drug Enforcement Administration (DEA), as presidents cannot unilaterally change the classification of controlled substances.

“It's not decriminalization,” Trump insists
When signing the decree in the Oval Office, Trump insisted that the measure does not represent federal legalization of marijuana for recreational use. He said he had been pressured by patients suffering from chronic pain who sought broader access to cannabis-based treatments.

“People were begging me for this,” the president said, stressing that the decision does not change the federal ban on non-medical use of the plant.

Trump, who claims to be teetotal, repeated on that occasion that he had always advised his children to avoid drugs, alcohol, and cigarettes, aware of the conservative ideological preferences of his most radical electoral base. The decision signals a shift in Trump's ideology, which has even drawn protests from American activists in favor of marijuana legislation, who attempted to distribute about 4,200 marijuana cigarettes on January 20, 2017, in Washington, during Donald Trump's first inauguration.

Millions of seniors may receive CBD-based products
The decision comes at a time when state legislation has been at odds with federal policy for years. Most US states allow the medicinal use of cannabis, and more than 20 of them have also legalized recreational use. This discrepancy has created a hybrid legal environment in which state-legalized companies face federal barriers, especially in accessing the banking system and tax restrictions.

The reclassification does not resolve these contradictions, but it does reduce obstacles to scientific research, which has historically been limited by the strict classification of marijuana in the first category of drugs considered dangerous. According to experts interviewed by US media outlets, the change may facilitate studies on therapeutic efficacy, addiction risks, and potential pharmacological uses.

The announcement should allow for more research on the risks of addiction associated with these substances, the authority said. In addition, “millions” of beneficiaries of public health insurance for people over 65 (Medicare) will be able to receive CBD-based products free of charge starting in the spring, announced Mehmet Oz, who heads the service.

Economic and regulatory repercussions
The cannabis industry, valued at tens of billions of dollars, welcomed the measure with relief. The reclassification is likely to reduce operating costs for licensed companies, while facilitating clinical research and the development of new drugs. It may also attract investments previously blocked by federal restrictions and reduce legal risks for growers and distributors.

The CBD market—already robust in the US—should benefit directly from the relaxation, which reduces regulatory barriers and may stimulate the entry of new pharmaceutical products. The executive order, however, has no immediate effect. The DEA is expected to conduct a technical and scientific review before formalizing the reclassification. This process could take months, but the presidential directive is pressuring the agency to expedite the review.