Developing a successful clinical trial supply chain relies on accurate forecasting. To understand which design elements and functionality are necessary to achieve your study goals, trial operators should carefully think through their entire supply chain. They should consider how their system reacts to various randomization designs, open-label and blinded products, multiple subject cohorts, dosing and visit schedules, and single or pooled investigational products (IP).
In addition to trial supply, it’s key to think through the data management process. A successful supply chain requires high-quality data to be shared across dispersed teams and sites to support real-time decision-making. Thinking through your data collection and information exchanges ensures issues can be identified and mitigated before impacting your actual study timeline.
Complex randomization and adaptive design require solution design flexibility. When that flexibility isn’t achievable, workarounds must be created in the supply chain that increases timelines and the risk for potential errors. It can also help you develop an agile approach to data management when mid-study changes occur.
To develop an adaptive approach to trial design, clinical trial operators should determine their master protocol and sync it with their RTSM system. The master protocol should cover the entire product development or randomization life cycle to minimize disruptions to the supply chain that inevitably lead to IP waste and increased costs. Below are the key considerations clinical trial operators should take when building out a master protocol for a successful RTSM strategy.
The most critical aspects of your trial are the foundational pieces on which the rest of the study will be set. Without being able to answer these questions, trial operators are destined to end up with screen failures and patient withdrawals.
Before you can build out the framework of your master supply strategy, clinical trial operators should be able to answer:
These questions are key to ensuring you create a study that appropriately addresses questions relating to the efficacy and effectiveness of the IP in question.
Once the essential elements of your trial are set, study operators need to ensure proper inclusion and exclusion criteria are established. Narrow criteria could create challenges in finding the right participants and make the results hyper-specific. In fact, exclusion criteria have become so specific that a quarter of trials exclude over 90% of patients, and 80% of trials excluded half of patients.
On the other hand, broad criteria could create challenges in detecting proper efficacy of an intervention. Be sure to strike a balance between generalizations and narrow exclusionary criteria to help with the reduction of bias. This criterion can best be established by determining the anticipated study outcomes and ensuring that your inclusion and exclusion criteria are in accordance with statistical support of those outcomes.
RTSM participant screening capabilities allow sites to capture demographic data and eligibility criteria before randomization and treatment assignment. To ensure a seamless participant screening process—and minimize delays in recruitment—participant screening questions should be considered while building the master study protocol.
Key questions and considerations trial operators must consider when outlining the inclusion/exclusion criteria and applying them to participant management include:
Randomized trials aim to understand and minimize the impact on the relationship between an intervention and its observed outcomes. Although a majority of randomized trials implement patient-level randomization, it may not be right for every scenario.
Unlike standard trials, randomization relies on data captured during the initial screening, consent, and enrollment process to aid in randomization assignment. Therefore, it is imperative to pre-determine your randomization schema and treatment plan early in the planning process.
From simple to blocked; stratified to sequential—clinical trial operators should use these questions as a starting point for their RTSM design:
Be sure your RTSM can support the intended randomization schema and then build out a test to ensure the system operates correctly.
An intervention’s success relies on its ability to be consistently delivered. The next critical step when creating a robust RTSM strategy is to consider the IP supply chain from initial production to final destruction.
When building out your trial supply strategy, clinical operators should consider each major area of their trial supply:
Randomized trials or studies with complex supply chains can produce a growing number of data points. RTSM, especially when integrated within an existing EDC, can help study managers monitor and control their data from a single system. If done effectively, study managers can create data management workflows that aggregate data from dispersed sites and teams to drive real-time decision-making that keeps studies on budget and on time.
It’s critical when building out the data management plan to consider what systems are collecting information, how it will be used, and what processes exist to safely share and monitor the data. On top of that, timely delivery of data is crucial for studies with complex supply chains. Instead of waiting days for approvals on IP dispensation after transport, study managers can reference live temperature and transport logs to confirm a drug is safe for treatment.
The key considerations trial operators and data managers should take when building out a data management plan include:
When building out a complex trial supply or randomized study, trial operators need a single solution to streamline their operations. With Medrio RTSM, you gain access to robust randomization and trial supply solutions that scale and fit any study need. No matter your study size, type, or randomization schema, our all-in-one solution helps trial operators save time and money while achieving operational efficiency. Plus, with access to a fully integrated solution, your teams get better oversight to high-quality, reliable data.