Managing Supply Disruptions In ATIMP Clinical Trials

By Nika Zigante, clinical supply specialist, MeiraGTx

The journey from drug manufacture to patient administration is rarely simple, but in the world of Advanced Therapy Investigational Medicinal Products (ATIMPs), it is especially complex and unpredictable.

Unlike conventional pharmaceuticals, ATIMP batches are inherently variable in stability and yield, with minimal opportunity for overproduction. As a result, every vial carries exceptional clinical and operational significance, where its compromise can directly impact its availability for patient treatment. Robust clinical supply chain operations from initial drug substance production through bulk storage, packaging, and distribution to clinical sites are therefore critical for efficient execution of clinical trials.

Within this complex environment, success is not defined solely by how well the supply chain performs when everything goes to plan but by how resilient it is when it doesn’t. Thus, the question is not whether something will go wrong but whether you are prepared to respond when it does.

Planning For Different “What If” Disruption Scenarios

Due to their high temperature sensitivity, limited shelf life, and strict regulatory requirements, ATIMPs are highly vulnerable to operational disruptions, which can significantly impact product availability for patient dosing. A structured “what if” scenario framework enables teams to anticipate risks, develop mitigation strategies, and maintain continuity of operations under a range of adverse conditions. This strengthens overall resilience and helps ensure uninterrupted patient access to treatment.

Identifying Potential Disruptions

Through systematic risk mapping, teams gain clear visibility into vulnerable points across the supply network. These may include:

• supply shortages due to in-house manufacturing constraints or supplier product availability
• temperature excursions during transit or storage
• transportation delays caused by customs clearance holds or geopolitical events
• constrained packaging capacity at suppliers
• country demand fluctuations driven by unpredictable patient enrollment or protocol changes
• unforeseen clinical site operational issues that put product at risk, such as EMS failures, freezer probe malfunctions, unreported excursions.

This visibility provides the foundation for developing effective contingency plans.

Developing Contingency Plans

Once risks are identified, contingency plans must be developed for each scenario to ensure operational continuity and minimize supply interruptions. These may include:

• establishing secondary packaging or logistics suppliers
• sending safety stock to selected locations such as depots or clinical sites
• designing flexible distribution models that can respond to country demand changes by supplying product to clinical sites from different depots
• implementing procedures for internal or site-based packaging capabilities, i.e., over-labeling, site-to-site transfers, or returns to depot.

The effectiveness of these contingency measures is then evaluated through structured risk assessments.

Performing Risk Assessments

Risk assessments play a key role in anticipating impact and prioritizing response actions. By evaluating both the likelihood and potential impact of each disruption scenario, resources can be allocated more effectively, ensuring attention is focused on the highest-risk areas. This structured approach also supports regulatory expectations for a documented, risk-based quality management system within clinical supply chain operations.

After Action Review (AAR)

While risk assessments help guide proactive planning and mitigation efforts, continuous improvement is achieved through learning from actual events. Following any disruption event, an AAR summary report should be conducted to systematically review what occurred, how effectively the response was executed, and what could be done differently in the future. Lessons learned from the AAR process are invaluable for improving future responses and enhancing overall clinical supply chain effectiveness.

Implementation of Process Improvements

Finally, corrective and preventive process improvements should be implemented based on findings from risk assessments and AAR outcomes. These improvements may include:

• creating or updating standard operating procedures
• increasing site monitoring visits or providing additional training for site personnel
• strengthening supplier qualification processes
• refining forecasting models to better understand demand and account for unpredictable changes
• improving cold chain and logistics monitoring capabilities to reduce transport-related risks
• implementing real-time temperature monitoring to react to excursions prior to delivery
• strengthening cross-functional communication and escalation pathways to enable faster response during disruptions.

Through continuous improvement, the ATIMP clinical supply chain evolves into a more robust and adaptive system capable of withstanding future disruptions while ensuring continued patient access to lifesaving therapies.

Using Real-Time Data To Predict Demand Changes

In addition to robust contingency planning, the integration of real-time data analytics plays an important role in predicting demand changes and enhancing responsiveness within the clinical supply chain. By continuously evaluating key decision-driving data, supply strategies can be adjusted proactively to reduce the risk of shortages. This is supported through monitoring of both demand and supply level indicators, including:

• country-level patient enrollment rates
• screening failure rates
• current site and depot stock levels
• current batch expiry dates
• upcoming  shelf-life extensions
• expiry date indicated on secondary labels
• secondary packaging or over-labeling lead times.

Beyond understanding the number of patients undergoing screening in each country and the corresponding screen failure rates, it is essential to define the most suitable approach for secondary label creation (country-specific or combined) and packaging for products with short shelf lives. Country-specific regulatory requirements related to labeling, such as whether expiry date information must appear on secondary labels, can significantly influence packaging decisions. When managing batches that have a short shelf life at the start of the trial, it may be pragmatic to consider developing country-specific labels separately, which can have a significant impact on labeling strategy and overall supply chain planning. This provides greater flexibility in managing inventory, while also reducing the risk of product wastage or the need for additional over-labeling with the new shelf life.

As clinical supply strategies become increasingly dynamic, digital tools are being used more heavily to support decision-making. Depending on available resources, budget, and trial scope or complexity, organizations may rely on manually maintained trackers to monitor patient enrollments and supply levels at a basic level. These can be further enhanced through visualization tools such as Power BI, enabling improved and automated data consolidation, trend analysis, and increased transparency in decision-making across functions. At a more advanced level, specialized demand forecasting software can be used to model complex demand shifts and support scenario planning. Finally, it is also important to acknowledge the increasing role of artificial intelligence (AI) in clinical supply chain analytics. These technologies enhance forecasting accuracy by identifying hidden patterns in historical and real-time data, thereby enabling more proactive and adaptive supply planning.

Conclusion

In ATIMP clinical supply chains, decision-making takes place in a highly uncertain and constrained environment. Effective supply chain management depends on the ability to integrate clinical, regulatory, and logistical information into timely, well-informed decisions. While structured planning, risk frameworks, and real-time data analytics provide valuable support, they must be applied in situations where conditions can change quickly and unpredictably. Organizations that successfully adopt a risk-based approach are better positioned to remain agile, reduce risk, and ensure uninterrupted supply to clinical sites for patient dosing.

As digital tools and AI-driven analytics continue to advance, they will enhance visibility and speed up decision-making. However, the most critical decisions will still rely on interpretation, context, and accountability that sit with people rather than systems. Technology can inform direction, but it cannot fully weigh nuance, responsibility, or patient impact in the way human judgements can. The future of clinical supply chain will therefore be defined not by automation alone but by how effectively it combines intelligent systems with experienced decision makers who understand when to trust the data and when to question it.

About The Author:

Nika Zigante is a clinical supply specialist at MeiraGTx, where she oversees clinical supply chain for global Phase 1-3 clinical trials with a strong focus on quality, compliance, and operational excellence. She began her career at MeiraGTx in quality assurance, gaining extensive experience in quality management systems and documentation oversight, which laid the foundation for her transition into clinical supply chain. She quickly discovered a strong sense of purpose in clinical supply and has since become deeply passionate about ensuring the delivery of life-changing treatments that improve patients’ lives. She is known for her persistence, approaching challenges with determination and a strong, solution-focused mindset.