Summary

Drug development is an arduous process marked by risk at each stage of the journey. Digital technologies have infiltrated nearly every domain of modern life, yet their application in medicine remains relatively untapped. Tools like wearable monitors that track vital signs, ingestible sensors that analyze the body’s response in real-time, and personal devices capable of detecting molecular reactions could upend drug development from concept to clinical trials. Presently, these innovative solutions collect vast troves of meaningful data yet see limited application in pushing past bottlenecks. With improved access to computational resources, they may transform high-cost, high-risk drug research by enabling iterative testing and personalized analysis at scale. Read this article to know more about why clinical trials need to be aligned with latest technology solutions. Join Rushford’s MBA in Clinical Research Management to gain comprehensive knowledge of the changing contours of healthcare industry!

Although testing new drugs is a lengthy and expensive process, we may soon see significant change with artificial intelligence. AI shows great potential to revolutionize clinical trials, ranging from more straightforward patient recruitment to improved adherence monitoring and automated data aggregation. These tools could streamline every step of the evaluation of promising therapies.

Almost 5,000 clinical trials have started over the last year to research life-saving treatments for and vaccines against COVID.

As a result of this industry-wide push to reduce the commercial price point of new drug programs from R&D-to-market, pharmaceutical and regulatory stakeholders are becoming gradually more receptive to novel trial designs, which include patient-centric studies, self-administration at-home dosing scan bags, alternative endpoints around real-world evidence (RWE)landmark agreements, Remote Data Collection & Telehealth touch-points. Technological advances have facilitated easy and automated real-time data capture, such as cheap sensors, wearables, etc.

Do you also believe this $52B clinical trial market needs a digital makeover?

From IoT for remote monitoring to machine learning (ML) workloads on electronic health records in flight, you name it – startups and big tech striving feverishly to provide comprehensive clinical trial solutions.

On average, 350 million individuals globally, or 8% of the total population, suffer from a rare disease, yet only around 5% of those cases have an approved therapy. The problem has essentially gone unaddressed, primarily due to the difficulty of making money out of something that occurs so infrequently. How do we provide additional authority to those getting little to no attention? Patients with these rare diseases have no alternate treatments, which puts them at risk of developing life-threatening health issues.

Through digital technology, researchers can now regularly monitor clinical trial participants’ health data daily while living their everyday lifestyles without significantly disrupting them. It helps get a more precise evaluation of the effectiveness of the research medication and the health of the individual. Additionally, they will drastically reduce the number of doctor visits that participants must make, which is incredibly challenging for those who work and is a significant cause of study dropout.

Regularly or continuously observing individuals’ reactions to potential medications in real time would provide clinicians with deeper insights into how such therapies impact illnesses and connect to general wellness. Traditionally, study participants have undergone testing or evaluation at predetermined checkpoints. Still, these periodic exams offer practitioners a fragmented view of how the examined substance influences conditions and meshes with one’s holistic health. Assessments once per week across a 12-week trial, for example, frequently fail to accurately predict a medication’s impact on an ailment or someone’s response to the experimental compound. Moreover, frequent monitoring might uncover interplays that scheduled appointments could miss, subtle shifts that accumulate into something more, or individual variances from average experiences. 

As the healthcare industry is undergoing a profound digital shift, why must we reform our approach to clinical testing?

Clinical trials demand an even greater level of transformation than we are witnessing in other areas of medicine currently. Progress in clinical study advancements and new treatment development has stalled for several years while research protocols have grown increasingly complex and burdensome.

The good news is that we have the solutions we need at our disposal. Harnessing information and optimizing digital tools invites us to explore more intelligent and innovative ways of operating. Whether it’s how we can boost the selection of molecules to introduce into early medical research portfolios or how we can leverage computerization to streamline compliance tracking without losing sight of patients’ well-being, opportunities abound if we open our eyes to reimagining old habits and transform the way we design, plan, and execute those trials.

On that note, here’s a bit of the good news. Clinical trials have been transformed over the past decade due to technological changes, regulatory guidelines, and a growing emphasis on patient-centricity.

Five important ethicotechnologies in progress for clinical trials

Digitalization of clinical data management 

Increased use of electronic data capture (EDC) systems and more global adoption of EHRs have consolidated document collection, enabling organizational efficiencies in trial management and reducing errors related to paper-based processes. This allows for real-time data analysis, and study conduct improvements such as remote monitoring ultimately increase the strength of trial oversight and patient safety.

Adaptive trial layouts

 Adaptive trial designs modify the protocol during execution in response to data of interest. They can thus improve efficiency by allowing adaptation on elements such as types of endpoints, treatment comparisons, or sample size configurations throughout a study, increasing chances for success. Adaptive designs also facilitate the ability to identify promising therapies or rapidly stop those not efficacious.

Patient-centric approach and inclusion

 The goal of patient engagement strategies is to integrate the input and needs of potential participants into the trial design. Hence, they are more “patient-centered,” – meaning that trials will be more relevant, pragmatic (effective in real-world settings), acceptable by patients to whom it may pertain, and contribute evidence that impacts how we care. This engagement translates into lower attrition rates, enhanced patient adherence, and more clinically meaningful results.

Virtual and Decentralized trials

This is a direct result of the COVID-19 pandemic; clinical trials have moved towards these decentralized and remote models. However, adoption has been pushed to warp speed due to the need for isolation during containment phases. The principles will benefit from approaches such as utilizing telemedicine, remote monitoring devices, and mobile health applications to reduce the number of patients visiting physical trial visits, making it easier for more people to participate. These trials can enhance patient enrollment, widen the demographic representation of participants, and provide more effective data collection.

Real-world evidence (RWE)

RWD and RWE are already being used to complement established approaches to the data generated from traditional clinical trials. It encompasses types, such as electronic health records (EHRs), wearable devices, and patient registries, among others, collected from different sources of RWD. Integrating RWD into clinical trials can help identify additional insights on a therapy’s effectiveness, safety, and long-term outcomes, enhancing understanding of how it affects real-world patients.

Virtual Trials and Digital Twins in Clinical Trials

While digital twins of trial participants allow researchers to simulate and predict outcomes, regulators are not keeping up with technological advancement.

The global digital twin industry is rapidly growing and expected to rise from $17.73 Billion in 2024 to an estimated value of over USD 259.32 billion by the year, further registering a CAGR of around 39%.

In this chaos of research seas comes the shining light: virtual trials and digital twins. They are part of a bold new era of medical discovery with the potential to redefine clinical trials. Digital trials (a concept increasingly gaining ground in the medical field) include clinical studies that patients can join remotely. This method is not only easier but also brings a variety of participants, making the collected data more enriching.

The idea of digital twins only furthers this transformation. Now, envision an intricate and digital rendition of the human body or one/or more of its systems uniquely designed to account for physiological adaptations and genetic nuances that are a hallmark feature in each patient. These digital creatures could be used to forecast how patients would respond to new therapies without the need for physical testing. This approach complements the revolution in AI and machine learning, providing a glimpse of future drug development, moving beyond being more efficient to offering increased personalization for individual patient needs.

 Digital twins in healthcare are an innovative combination of technology and science unlike anything else. Fundamentally, they are complex digital simulations of the physiology and genetics of patient-specific or system-specific. Developed using the patient’s imaging data, these digital twins are intended to model and foresee any response for a given management or medical procedure. Digital twins are advancing care delivery by leveraging real-time data feeds, machine learning, and augmented reality to change how healthcare professionals diagnose and treat patients.

Some of the first initiatives that have witnessed traction include GE HealthCare’s Command Center as a demonstration to identify and apply digital twins for operational strategy and care delivery models within healthcare. By constructing virtual hospital models, the impacts of various decisions – e.g., bed placements, surgical schedules, and facility designs- on organizational performance can then be studied.

In practice, this means that doctors could one day test a new drug on the digital twin of an individual before giving it to them – and see how their body may respond. Patients develop individual therapy to eliminate the costly need for human and animal studies, which speeds up drug development. It also improves patient safety by testing virtual treatments with less chance of complications in the real world.

What is the bad news? 

It is still a difficult task to conduct efficient and cost-effective trials. Clinical trials are complicated, so the more confusing a trial may be due to the complexity of its design, which can make it difficult for the company to design and select endpoints. Finding clinically relevant and statistically sound endpoints that satisfy regulatory standards for efficacy while still being patient-centered will always be a struggle. Moreover, the benefit of adaptive trial designs also comes with added complexities in planning and executing a controlled clinical study primarily conducted across the industry. However, this latter challenge would be alleviated through the involvement of skilled biostatisticians experienced with complex trial designs.

From Here, Where Do We Go?

As a result, Continuomics will permit the design of many new types of clinical trials with more focused-arm interventions that are shorter in duration and involve fewer participants — all at a lower cost yet potentially enabling exciting therapies to be brought to market sooner than permitted by current guidelines.

So, clinical trials are just the start. Continuomics is a new word for a future vision of more effective and personalized treatment. It helps in making predictions with great precision the likelihood that someone might get certain diseases or their response to given therapies based on who they are, meaning much more than just genes! Continuomics can enable previously unthinkable levels of personalized diagnosis and treatment by connecting data on a person’s genotype and phenotype.

Transforming health care is not a quick or easy task. But for that matter, we are beginning instead to see some possibilities emerge that will make way for digital technologies in possible ways. A growing suite of leading drug manufacturers has also shown an interest in integrating digital technology into their early-stage and Phase II trials.

Join Rushford’s MBA in Clinical Research Management to know more about clinical research and its far-reaching impacts on human lives!