As clinical trial designs evolve, the clinical supply plan can become increasingly complex. This is particularly evident with the adoption of novel trial designs, such as master protocols and the emergence of direct-to-patient (DtP) clinical supply chain needs. This complexity extends across all systems used in clinical trials, including Randomization & Trial Supply Management (RTSM) and Interactive Response Technology (IRT).
Traditional Role of RTSM in Clinical Trials
The industry has come a long way since the early days of randomization and trial supply, when manual systems (remember numbers in envelopes!) were used to assign patients to groups and patient-numbered complete kits were shipped to all sites. Not only did we spend an inordinate amount of time managing these processes, there was also inherent wastage in the system, not to mention human error. Early automation of these processes (i.e., through IRT) focused mostly on patient randomization and providing initial drug supply to the sites.
Fast forward to today, and we’ve realized that trial supply management is just as critical as randomization, prompting the creation of highly capable RTSM systems. Now, an RTSM manages the protocol and supply chain by enabling multiple processes, such as:
- Randomizing patients
- Dispensing drugs
- Ensuring sites are resupplied with the appropriate amount of drug at the right time
- Tracking of temperature excursions relating to investigational products during shipping
- Maintaining study blind and emergency unblinding
- Drug reconciliation and destruction
And, with integrated Trial Supply Optimization (TSO), you get the added benefits of scenario planning and forecasting, lot planning, and reforecasting and optimization. Although IRT and TSO functions can operate independently, the combination of the two provides powerful capabilities to cover the full cycle of supply management from planning and forecasting through protocol inventory management, temperature event management, and drug reconciliation and destruction. It’s possible to model supply scenarios, predict future demand, and optimize supply chain settings to ensure timely delivery of materials and minimize wastage.
Just when we thought we could confidently optimize trial supply, clinical trial designs have become more sophisticated, requiring the expansion of clinical trials systems, such as RTSM.
Clinical Trial Evolution
Adaptive trials and master protocols
Adaptive designs allow for trial adjustments based on information that wasn’t available when the study started. Benefits of this include:1
- A greater chance of detecting a true drug effect
- Achieving the same statistical power with a smaller sample size or shorter duration
- The ability to stop a trial early if the data show that effectiveness is unlikely to be demonstrated during the trial
- Determining the effectiveness in a targeted subgroup
- Better estimates of the dose-response relationship
At the same time, master protocols evolved in oncology studies to test multiple investigational drugs, multiple target indications and/or multiple cancer subpopulations in parallel using a single protocol.2 Implementing a master protocol avoids having to develop new, trial-specific protocols. Master protocols are inherently highly adaptive designs executed in long-term studies, often at large scale, during which the outcomes are continually analyzed to adjust the protocol design variables, as necessary. There are three types of master protocols: umbrella, basket and platform, which vary by the number of drugs and disease populations being studied in a single trial.
Both adaptive and master protocol approaches require highly sophisticated RTSM design capability coupled with the ability to introduce protocol adjustments quickly and efficiently during study execution.
A shift to large molecules
As drug R&D shifts from chemical-based, small-molecule therapeutics to complex and often more effective large-molecule biologics, we must consider the effects of this shift on distribution and storage. Large-molecule drugs are typically less stable than small-molecule drugs, requiring complex cold-chain logistics and temperature-controlled conditions.
Even with advanced logistic and temperature control systems in place, drugs may fall outside the temperature range during transit or storage, a complication that should be determined before administering the drug to the patient. Therefore, it is important to leverage temperature monitoring data at the point of dispensation. To avoid wastage and minimize the risk of stock-outs, RTSM-enabled forecasting and supply chain optimization are particularly important.
Emergence of rare diseases and orphan indications as therapeutic targets
It’s easy to assume that rare diseases affect relatively few people. This is true at the individual disease level, but an estimated 7,000 rare diseases affect 25-30 million Americans,3 more than half of whom are children. Despite the often life-threatening nature of rare diseases and the number of lives they affect, maybe only 5% have FDA-approved treatments.4 To improve approaches to the diagnosis and treatment of rare diseases, the National Institutes of Health awarded 31 million USD in grants in 2019 to 20 separate research teams, in addition to 7 million USD to a data coordinating center to support the teams’ research. And, Tufts University recently reported that drug development for rare diseases now accounts for one-third of the worldwide R&D pipeline.5
One challenge with finding treatments for these diseases is that the small number of people affected by each disease inhibits understanding of even basic information about symptoms, biology, and long-term outcomes. Regarding the use of RTSM to support clinical trials and the scarcity of subjects, studies tend to be conducted with very few subjects per site, increasing the precision required for randomization and supply management. And, because many of these studies are part of accelerated clinical development programs, accelerated RTSM start-up is also required.
Other patient populations that present difficulties for clinical trials consist of people with limited mobility or in remote locations relative to clinical sites. Clinical-trial-at-home approaches increase reach to these populations, but they require your RTSM solution to manage DtP distribution, which involves delivery to and administration of investigational products or devices at the patient’s home. Since DtP approaches reduce the dependency on the patient to visit sites, they’re improving recruitment and retention rates in trials involving geriatric, pediatric, and rare disease protocols, as well as subjects with limited access to sites.
DtP may also be employed in protocols with investigational products of high value, strict temperature and stability requirements, and personalized machines. However, there are often evolving regulatory considerations, and some geographies are more restrictive with DtP. Therefore, in a multinational trial, DtP might be used in some countries but not in others, further complicating trial supply management.
An Evolving Conversation
Much has been written about increasing complexity in clinical trials, but the effect of this changing landscape on RTSM, specifically, is something we will need to continue to address with improved processes and technologies. The history of clinical trials demonstrates the necessity of continually adapting our systems for future trial designs.
Next week, I’ll be with my colleagues at SCOPE 2020 in Orlando, Florida, continuing this conversation, including how RTSM can expand to meet the needs of novel clinical trial designs, during the Clinical Trial Management track. Join me during my presentation discussing Considerations for Use of IRT in Novel Clinical Trial Designs on Thursday, February 20th at 9:25 a.m.
Not attending SCOPE? Check back here afterwards, where we’ll have part 2 of this blog series describing important considerations when using IRT and supply chain forecasting/optimization systems in each of these newer supply paradigms.