Study Start-Up Is Where Clinical Supply Risk Is Actually Decided
By William Bryant
When a clinical trial experiences investigational product (IP) shortages, delayed shipments, or emergency resupply requests, teams often assume the clinical supply chain failed. Manufacturing delays, depot capacity, packaging timelines, and distribution logistics usually receive the greatest scrutiny. These functions matter, but they are rarely where the underlying risk begins.
In many studies, the supply challenges that disrupt execution take shape months earlier during study start-up. Country selection, feasibility assessments, enrollment assumptions, site activation timelines, regulatory pathways, and protocol complexity quietly define future demand on the clinical supply chain. By the time the first supply issue becomes visible, the operating conditions that created it are already built into the plan.
Clinical supply leaders are often asked to solve problems that originated outside their function. Aggressive enrollment targets, optimistic activation schedules, evolving protocol requirements, and country-specific regulatory requirements influence manufacturing forecasts, depot strategies, import planning, and distribution design. When teams make these decisions without sufficient cross-functional review, supply teams react to risk instead of preventing it.
As trials become more global and operationally complex, sponsors have an opportunity to shift from reactive supply management to proactive supply planning. ICH E6(R3) reinforces quality by design, proportionate risk management, and critical thinking throughout the trial life cycle.1 Those principles apply directly to clinical supply. Integrating supply strategy into study start-up helps teams identify avoidable risks before they affect manufacturing, distribution, inventory, or patient enrollment.
Why Clinical Supply Problems Start Before Supply Teams Become Involved
Many organizations still treat clinical supply planning as a downstream activity that begins after protocol finalization. In reality, the assumptions that drive supply forecasts emerge much earlier. Feasibility teams estimate enrollment. Clinical operations teams shape activation timelines. Regulatory teams interpret country pathways. Manufacturing teams then receive a demand signal that already reflects those assumptions.
During study start-up, each strategic decision creates a supply consequence. Country selection determines import requirements, labeling needs, depot locations, customs documentation, and shipment lead times.2 Feasibility assumptions determine how much product manufacturing should produce and when it should be released. Site activation plans determine when IP must be available locally.4 Protocol design determines visit frequency, dosing duration, kit configuration, comparator needs, and resupply triggers.
The cause-and-effect relationship is straightforward but often underappreciated. If feasibility overstates recruitment, manufacturing may release more product than the study can use. If country selection adds complex import pathways, depot inventory may not reach sites when activation occurs.2 If regulatory timelines shift but supply forecasts remain static, inventory can expire in one region while another region waits for product. These are not isolated supply failures. They are planning failures that become visible through supply execution.
Consider an anonymous sponsor selecting several countries with historically lengthy IP import timelines. Clinical operations may view the decision as reasonable because patient access, investigator experience, and enrollment potential appear strong. However, if manufacturing, labeling, depot allocation, and import documentation are not adjusted to reflect those timelines,2 the study builds in a delay before product is even packaged. The downstream effect may include delayed first patient in, emergency shipments, higher logistics costs, and compressed enrollment recovery plans.
Enrollment assumptions create similar risk. If projected enrollment is too high, IP may be manufactured and distributed before sites are ready to use it. Inventory then sits in depots, consumes shelf life, and increases the chance of expiry. If actual enrollment exceeds expectations, the opposite occurs. Sites consume inventory faster than planned, resupply settings lag behind demand, and teams may need emergency shipments or forecast revisions.
These examples illustrate a fundamental reality of modern clinical development: clinical supply teams frequently inherit risks unknowingly created during study planning. A more resilient model brings supply leaders into study start-up before those risks become embedded in the operating plan.
Country Selection: The First Hidden Supply Decision
Country selection is typically viewed through the lens of patient recruitment, investigator experience, regulatory timelines, and study feasibility. Yet every country selected also represents a unique supply chain environment with its own operational requirements.
Each country introduces different import regulations, customs clearance processes, labeling requirements, temperature control expectations, depot options, and documentation needs.2 Some markets require additional import approvals or local release steps before IP can enter the country. Others permit a more streamlined process. EMA guidance on sponsor responsibilities for handling and shipping investigational medicinal products underscores the importance of release, shipping, technical agreements, and clear sponsor oversight.2
These country-level differences influence far more than shipment timing. They affect when manufacturing must complete batch release, when packaging artwork must be finalized, how depots allocate inventory and whether regional distribution can support the activation sequence.2 A country that looks attractive during feasibility may create supply risk if its import pathway does not match the study’s activation expectations.
Consider a global study preparing to activate sites in a country where IP import approvals routinely require several additional weeks beyond the original assumption. Clinical operations may complete regulatory submissions, contracts, training, and activation activities on schedule. Yet if IP cannot clear customs in time, activated sites cannot enroll patients. The milestone appears achieved, but the study cannot execute.
Another common scenario occurs when a sponsor allocates inventory to a regional depot based on expected country activation. If one country experiences a regulatory delay while another country activates early, the depot may hold product in the wrong location. Redistribution may not be simple because relabeling, import restrictions, temperature controls, or documentation requirements can limit movement.2 The result is excess inventory in one region and constrained supply in another.
This is why country selection should not be viewed solely as a feasibility decision. It is also an early clinical supply decision with downstream consequences for manufacturing, depot planning, inventory management, distribution strategy, and patient enrollment.
Enrollment Assumptions And Forecasting Risk
Enrollment forecasts are among the most influential assumptions made during study start-up. They determine manufacturing quantities, packaging schedules, depot inventory, resupply parameters, comparator sourcing, and overall supply availability. Yet enrollment projections remain uncertain because they depend on future patient identification, site performance, competing trials, protocol burden, and activation timing.3
Forecasting becomes especially challenging when studies involve new investigative sites, novel therapeutic areas, or countries with limited historical enrollment data. Recent benchmarking literature continues to show that site activation and patient enrollment performance vary across development programs, regions, and operational models.3 Even modest deviations between planned and actual enrollment can create meaningful clinical supply consequences.
Consider an anonymous Phase 3 study expected to enroll 25 patients per month across multiple countries. Manufacturing plans, depot inventory, and interactive response technology settings all reflect that assumption. Site activation then progresses more slowly than expected and enrollment averages only 10 patients per month during the first six months.3
The study may eventually recover, but IP manufactured early remains in storage far longer than planned. Inventory accumulates across depots, storage costs increase, and product expiry becomes a growing concern. Teams may need expiry extensions, stability assessments, redistribution plans, or revised manufacturing schedules.2 None of those activities were part of the original execution plan, yet all stem from an early enrollment assumption.
The opposite scenario creates equal pressure. Several high-performing sites may activate together and enroll faster than expected. Supply forecasts based on conservative assumptions quickly become outdated. Depots experience rapid inventory drawdown, resupply triggers fire more often than planned, and manufacturing may not have enough lead time to replenish stock. The study then relies on expedited shipments, manual inventory interventions, or emergency manufacturing campaigns.
Comparator sourcing can amplify the risk. If enrollment accelerates and comparator lead times are long, the study may have sufficient investigational drug but insufficient comparator product. That imbalance can delay randomization even when the sponsor’s product is available. Supply planning must therefore evaluate total kit readiness, not just availability of the sponsor’s compound.
When Activation Timelines And Supply Timelines Collide
Study activation and clinical supply planning often move along parallel tracks. Problems arise when those tracks begin moving at different speeds.
Clinical operations teams focus on activating sites as quickly as possible. Success is measured through regulatory approvals, executed contracts, ethics committee approvals, training completion, and site activation milestones. Clinical supply teams work against manufacturing schedules, quality release, packaging timelines, depot preparation, import approvals, and temperature-controlled distribution requirements.
When these functions do not update assumptions together, organizations may activate sites without ensuring IP is available when patients are ready. Federal regulations also illustrate why timing matters: an investigational drug may be shipped only under the conditions that allow lawful use in a clinical investigation.4 Regulatory, release, and shipment readiness must therefore align with site readiness.4
Imagine an anonymous global study that activates multiple sites several weeks ahead of schedule. From an operational perspective, the study appears to be outperforming expectations. However, manufacturing timelines were built around the original activation plan and cannot accelerate. IP remains in production while activated sites wait to screen and randomize patients.
Alternatively, IP may arrive at depots on time while regulatory reviews or contract negotiations delay activation. Product then sits unused, consuming shelf life before the first patient is enrolled. If the product has a limited expiry window, the study may later face preventable waste or replacement manufacturing.2
Neither situation reflects poor performance by clinical operations or clinical supply independently. Both demonstrate the consequences of misaligned planning assumptions. Activation forecasting, import forecasting, inventory forecasting, and manufacturing forecasting must move together as study start-up evolves.
Successful organizations reassess activation forecasts alongside supply forecasts. They understand that a change in site readiness affects depot allocation, distribution strategy, inventory levels, and patient enrollment. The goal is not to slow activation. The goal is to ensure activation creates enrollment capacity rather than another operational bottleneck.
How Predictive Analytics And AI Can Improve Early Visibility
Traditional study planning relies heavily on historical averages, static spreadsheets, and periodic manual reviews. These approaches remain useful, but they often identify risk only after assumptions begin to diverge from reality. Predictive analytics can help teams see those divergences earlier and translate them into operational decisions.1
Enrollment forecasting provides a practical example. Rather than relying on a single feasibility estimate, a predictive model can continuously compare planned enrollment with site activation progress, screening rates, screen failure trends, competing studies, investigator history, and protocol burden.3 If the model shows that enrollment will lag, supply teams can delay future manufacturing, adjust depot inventory, and reduce expiry risk. If the model shows acceleration, teams can increase resupply frequency, initiate additional packaging, or review comparator availability before shortages occur.
Activation forecasting works the same way. Models can evaluate contract cycle times, ethics committee patterns, regulatory approval durations, document completion, training status, and prior site performance.3 Those outputs help clinical operations and clinical supply agree on when a site will actually be ready to enroll. Supply teams can then stage product based on probable activation, not aspiration.
Import risk prediction is another high-value use case. A model can combine country-specific import history, required documentation, product type, temperature requirements, depot location, local holidays, and regulatory dependencies.2 If a country shows elevated import risk, teams can submit documentation earlier, adjust depot allocation, increase buffer stock, or revise activation expectations before sites are affected.
Predictive analytics also supports expiry management. Models can evaluate current inventory, remaining shelf life, projected enrollment, country activation probability, and resupply cadence. When the model identifies inventory likely to expire before use, teams can redirect stock, slow future manufacturing, adjust allocation, or prioritize use in regions with active demand.1
Manufacturing forecasting benefits when these inputs connect. Instead of producing against a fixed enrollment curve, manufacturing can respond to updated demand signals from activation, enrollment, import readiness, and inventory consumption. This reduces overproduction, protects limited shelf life, and helps avoid late emergency campaigns.
Depot optimization and resupply forecasting offer additional value. Analytics can show where inventory should sit based on country risk, site readiness, patient demand, lead time, and temperature control constraints. The goal is not to move every kit closer to every site. The goal is to place inventory where it has the highest probability of timely use with the lowest probability of waste.
Country risk modeling can further support governance. By combining regulatory duration, import approval timing, activation performance, enrollment reliability, and logistics constraints, teams can rank countries by operational risk. That ranking should not automatically exclude a country. It should prompt earlier planning, realistic timelines, targeted mitigation, and transparent trade-off discussions.
What Clinical Supply Leaders Should Do Differently
Clinical supply should participate in country selection, feasibility reviews, enrollment forecasting, regulatory planning, comparator strategy, and activation planning. Early involvement gives teams visibility into operational assumptions before those assumptions become locked into the study plan.1
Supply leaders should also challenge forecast certainty. A single enrollment curve is rarely enough. Teams need scenarios that show what happens if activation slips, enrollment accelerates, import approval is delayed, comparator sourcing takes longer, or product expires sooner than expected. Scenario planning turns uncertainty into manageable options.
Cross-functional governance should connect clinical operations, feasibility, regulatory affairs, clinical supply, manufacturing, quality, and logistics. These discussions should not occur only at major milestones. They should occur whenever assumptions change, because one revised timeline can alter manufacturing plans, depot allocation, import strategy, and site readiness.1
Finally, sponsors should use predictive analytics as decision support, not as a substitute for operational judgment. The value comes from earlier visibility. When teams can see the probable effect of enrollment, activation, import, and inventory changes, they can act before risk reaches patients and sites.
By combining operational expertise with predictive tools, organizations can reduce uncertainty, improve planning accuracy, protect limited shelf life, and create more resilient clinical supply strategies.
Conclusion
Clinical supply challenges rarely begin within the supply chain itself. More often, they originate during study start-up through decisions about country selection, enrollment assumptions, activation timelines, regulatory pathways, import requirements, and operational planning.
When teams make these early decisions without sufficient collaboration, sponsors increase the likelihood of downstream manufacturing changes, IP shortages, emergency shipments, inventory waste, depot imbalances, and delayed patient enrollment.1,2,3
The solution is not simply better logistics. It is earlier collaboration, greater operational visibility, realistic forecasting, and continuous reassessment throughout study start-up.
Predictive analytics and AI can help, but only when teams connect the insights to practical decisions. Forecasts should influence when product is manufactured, where inventory is placed, how depots are stocked, when comparator is sourced, and how country risk is managed.1,3
Clinical supply risk should no longer be viewed as a problem to solve after study start-up. It should be recognized as a strategic consideration that begins with the earliest decisions made during clinical trial planning.
References
1. International Council for Harmonisation. ICH E6(R3) Guideline for Good Clinical Practice. Final version. Adopted January 6, 2025.
2. European Medicines Agency. Guideline on the Responsibilities of the Sponsor With Regard to Handling and Shipping of Investigational Medicinal Products for Human Use in Accordance With Good Clinical Practice and Good Manufacturing Practice. EMA/INS/GMP/258937/2022. Published September 14, 2022.
3. Tufts Center for the Study of Drug Development. Benchmarking site activation and patient enrollment. Therapeutic Innovation & Regulatory Science. 2024.
4. Electronic Code of Federal Regulations. 21 CFR § 312.40, General requirements for use of an investigational new drug in a clinical investigation.
About The Author:
William Bryant is a clinical research professional with more than 20 years of experience in global clinical operations, study start-up, and strategic trial execution. He has led Phase 1 through Phase 3 clinical studies across multiple therapeutic areas, including oncology, central nervous system (CNS), infectious diseases, metabolic disorders, and vaccines. His expertise includes global feasibility, site activation, regulatory strategy, operational risk management, and cross-functional program leadership. William is passionate about improving clinical trial performance through operational excellence, predictive decision-making, and the practical application of emerging technologies such as artificial intelligence.