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.
Trial Basics
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:
- What is the final number of sites?
- What is the final number of expected depots?
- What is the expected trial length (from FPI to database lock)?
- Is the trial randomized or open-label?
- Who are the key stakeholders?
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.
Defining Your Inclusion and Exclusion Criteria
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.
Participant Management
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:
Visit Date and Participant Screening
- What demographic data is needed for screening?
- Basic: age, gender, informed consent
- Advanced: Cohort or treatment-specific determining data
- What visit capture information is needed before, during, and after?
- Are single or multiple eligibility check questions necessary to confirm inclusion and exclusion criteria are met?
- Do patient screenings need an independent review and approval by a Medical Monitor before randomization assignment?
Dosing and Dispensing
- What are the pre-dosing eligibility criteria?
- What are the configurable dosing considerations?
- What is the preferred cadence for progress tracking IP doses?
- What is the escalation plan for missing or tampered IP?
- Are any dosing expansions needed such as Single Ascending Dose (SAD) or Multiple Ascending Doses (MAD)?
- Are there any dose calculation/modification rules that might affect dispensing rules?
Patient and Cohort Management
- What is the enrollment requirement?
- Will subjects be managed in separate cohorts or treatment arms?
- What is the process to identify, record, and mitigate AE/SAEs?
- How will patients receive dosing instructions?
- What is the process for informed consent?
- Do any patient advocacy groups need to be involved in building the master strategy?
- What data needs to be collected from patients, how will it be collected, and how often?
- Will patient-reported outcomes be used?
Randomization & Treatment Assignment
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:
- What randomization design will best support the intended investigation: simple, blocked, stratified, covariate adaptive, minimization, sequential parallel comparison design (SPCD), responsive adaptive, or a combination?
- What are the stratification factors?
- What is the control variable – placebo or active?
- How many patients are expected to benefit from the intervention?
- Is there a crossover design for treatment assignments?
- Does the trial design require a single, double, or triple-blind approach?
Be sure your RTSM can support the intended randomization schema and then build out a test to ensure the system operates correctly.
Trial Supply Management
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:
IP Packaging
- Weigh packaging options against key factors including product type, patient compliance, site compliance, blinding, cost, country-specific requirements, visit schedule, shelf life, expiry, and destruction.
IP Delivery & Dispensation
- What is the timing of interventions?
- What is the schedule of monitoring visits?
- What are the IP doses and dose modifications for each patient visit?
- Will kits be sent direct-to-site or direct-to-patient?
IP Storage & Resupply
- What are the minimum and maximum storage capabilities at each site and depot?
- What are the IP storage and maintenance requirements?
- How much time does it take to fulfill a resupply order?
- What trigger will notify sites of a resupply?
- Do you want to manually place inventory orders or have the system perform just-in-time (JIT) automated inventory resupply orders?
IP Return & Destruction
- What is the standardized protocol for IP return, and resupply across sites?
- Is there a requirement to manage the reconciliation of products to the level of kit contents inside the package?
- Can IP be disposed of safely by the sites or patient?
Data Management & Compliance
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:
- Data Collection—What systems are collecting data points and what data points are necessary for testing the efficacy and effectiveness of the IP?
- Data Sharing—How will the systems in your trial supply and randomization share data? Can these systems integrate and/or enable real-time data sharing?
- Data Monitoring—Is there a central monitoring system? How can sponsors, CROs, and study managers monitor data for on-the-spot decision-making?
- Reporting—What types of reports are necessary for all clinical trial stakeholders and to conduct proper analysis? What custom reporting is needed to disseminate final findings?
- Data Compliance—What are the country and region-specific regulatory requirements? Are the systems collecting data points secure and compliant?
A Single Solution for a Successful RTSM Strategy
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.
