Clinical trials, known for their inherent chaos, have experienced a significant surge in numbers over the past two decades. From a mere 1,000 studies registered in 2000, we now find ourselves grappling with a staggering 400K+ trials as of 2020 — and that number is only increasing. However, it's not just the sheer volume that poses challenges.
Within the realm of clinical operations, current financial pressures, staffing shortages, resource constraints, and intensifying competition for sites and patients create a perfect storm. We find ourselves in the constant pursuit of accomplishing more with limited resources, putting immense strain on the entire process.
But it doesn't stop there.
Study design complexity has also been steadily on the rise. In fact, we're witnessing an 86% increase in endpoints, 88% more assessments, and a whopping 70% surge in procedures including biospecimens. This surge amplifies the operational and logistical intricacies, further compounded by the critical need for efficient biospecimen collection and overall sample management.
Why are biospecimens so crucial?
Biospecimens hold the key to unlocking the data that propels a trial forward. As the number of endpoints, assessments, and procedures multiply, the demand for samples follows suit — growing exponentially.
Now, you may be wondering: which types of trials rely most heavily on biospecimens?
In this blog, we explore the top five types of clinical trials where sample management is an absolute necessity for study success. Join us as we explore the intricate interplay between sample management and these complex trials, uncovering the pivotal role they play in advancing medical research and improving patient outcomes.
Oncology trials are arguably one of the most complex clinical trials. Where we once were running 7 different studies, with 7 different indications, across combined study treatments, we are now lumping these into one study with several underneath — a basket trial design. Another design that is now being employed is the umbrella trial — where patients with the same cancer may receive a different treatment based on their specific mutation or biomarker.
Each arm of the umbrella trial could enroll 20 patients. If you are studying 5 different mutations, you are enrolling 100 patients. Let’s assume each patient has 5 visits, and one sample is collected for each visit. That sample is then aliquoted and sent to 5 different speciality labs at different times and temperatures for analysis. That equates to 2,500 samples — and almost all trials require more than one sample per visit.
Data from these samples are critical for enrollment criteria, safety and efficacy decisions, and in early phase research, inform the outcome of dose escalation plans. Not to mention, oncology trials are treating very sick patient populations. Having to repeat a procedure due to a lost or mishandled sample can be prohibitive for many of these patients. It is so important to have an airtight, traceable chain of custody for every sample.
Rare disease trials have smaller patient populations, and those patients can be scattered globally for any given trial. Given that rare disease patients may travel significant distances to participate in a study, asking the patient to return isn’t a reasonable option. In addition, if a sample is mishandled this could result in exclusion of a patient which these studies cannot lose. In many cases, rare disease patients also require the assistance of a caregiver, which adds to the burden of participation.
Along with the small number of patients is limited data. Although regulatory agencies have instituted guidance to support these studies, there is a struggle to obtain enough data to qualify biomarkers for regulatory decision making.
As detailed in our conversations with Duchenne Muscular Dystrophy (DMD) patient Billy Ellsworth, we cannot underestimate the impact of the sample procedures like muscle biopsies, which can lead to painful recovery. Rare disease trials are another trial type where sample management can either facilitate progress or completely halt a study from progressing.
Personalized medicine trials are on the rise. For example, there are over 1,500 cell and gene therapies in development according to ClinicalTrials.gov.
There are two main types of cell and gene therapy trials: allogeneic and autologous. Allogeneic therapies are manufactured in large batches from unrelated donor tissues (such as bone marrow) whereas autologous therapies are manufactured as a single lot from the patient being treated. Both types rely heavily on biospecimens, where the sample itself then becomes the therapy for the patient. With bone marrow (allogeneic), it is critical to ensure there is a match from the donor to the patient. With autologous, the biospecimen is taken from the patient and after the specimen is altered, it then needs to go back to the exact patient that it originated from. The need for a 100% traceable chain of custody from the point of collection, processing, and back to the original patient is non-negotiable for these trials.
These studies, and the samples themselves, are very expensive. Proper tracking, storage, handling, and data integrity is important for both patient safety and trial outcomes.
Early-phase clinical trials are also increasingly complex, despite the lower patient populations. Beyond the oncology trials mentioned previously, let’s not forget the other therapeutic areas that fall into this category: Immunology, CNS, and even vaccine trials – to name a few.
The complexity is driven, in large part, by the sample-intensive Schedule of Events. An early phase study's primary, secondary, and (in many cases) exploratory objectives require extensive research sampling such as pharmacokinetics, pharmacodynamics, and pharmacogenomics, which translates to multiple sample collections per visit. Even with fewer total patients enrolled in these early-phase studies, the total number of samples collected across the trial adds up quickly.
While the first four examples focused primarily on study design/protocol company, our last study type on the list centers around data, operational, and logistical complexity. With large, global phase III trials, you can easily have upwards of 10,000 patients enrolled – which compounds to a large volume of samples. While each individual sample may not be as crucial (outside of the impact on the patient), there are massive amounts of sample data that must be reconciled at the end of the study for regulatory filing. Not to mention, there are a multitude of stakeholders involved in the process which supplies pieces of the sample data and history.
No matter what type of clinical trial you are running, if there is a reliance on biospecimens, there is an added layer of complexity. From oncology and rare disease trials, to cell and gene therapies, early-phase studies, and large-scale global trials, the challenges vary significantly. However, by implementing comprehensive sample management protocols and leveraging advanced technologies, trial sponsors, researchers, and stakeholders can overcome these complexities and ensure the integrity, traceability, and timely analysis of samples.
Effective sample management is a cornerstone of reliable data generation, ultimately driving the development of safe and effective treatments for patients in need. For more information, visit slopeclinical.com.