Calyx IRT was the system of choice for a study involving patients admitted in intensive care units (ICU) due to complications linked to COVID-19 infection. The study was run through a CRO, and Calyx worked directly with both the CRO and the sponsor team.

On paper, the study was a relatively simple IRT design, investigating whether the study drug would reduce the time spent in ICU. Patients were enrolled in the study upon admission, treated in the ICU, and dosed up to 6 times over the course of 28 days or until discharge, whichever was earliest. There was an established depot network from the study start and supplies to sites were based on expected recruitment rates.

However, clinical trials rarely go to plan, do they?!


During trial execution, the sponsor had to switch distribution vendors, as they were not satisfied with the one selected initially. The switch had to be done in flight, while sites were still recruiting, without impacting supplies to sites and patient recruitment.

For the last 10 months of the study, the sponsor faced a critical situation regarding medication management. Due to a longer start up period than initially expected the medication in stock at depots and sites was about to reach expiry limits. Without any additional stock and no time to manufacture more medication, they risked not being able to supply patients with medication, which would have resulted in the trial closing before reaching the required sample size.


The Calyx project manager leveraged their experience managing similar situations to address the challenges the sponsor faced.

Switching Distribution Vendor

Calyx IRT includes standard integrations for both distribution vendors that were used in this study. Switching vendors mid-study required changing the depot network, as well as applying different integration standards, to support ease of use for the new distribution vendor.

Thankfully, Calyx IRT inventory management includes flexible settings made to deal with unforeseen situations like this one. We used those settings to increase the stock of medication at sites, anticipating that stocks would need to last longer during the depot transition period.
We supported the clinical supplies team with transferring medication to the new depots and releasing the medication for use once it was confirmed to be available at the new depots. The depot transition and new integration were invisible for sites.

The Calyx project manager was instrumental in advising the CRO and sponsor how to navigate this situation, recommending the most suitable options available. They coordinated the various updates required and released them all at the right time, in alignment with the physical move of the medication stock.

Using Medication Close to Expiry

Situations where medication is reaching expiry dates are tricky. Calyx IRT inventory management includes flexible Do Not Ship and Do Not Dispense settings, which can be amended down to patient dispensing level if required. We would usually recommend not amending those settings, but our team has the right level of expertise to implement those updates safely.

For this study, the Calyx project manager worked closely with the clinical operations team whenever a patient needed medication. This included actions to ensure that patient status within the IRT were proactively updated to reduce the risk of predicting too much medication, therefore increasing accuracy of shipments. They demonstrated excellent communication and prioritization, ensuring inventory management settings were amended immediately and appropriately to allow medication dispensing to patients. The Calyx project manager kept the study team informed throughout the process, allowing the team to set expectations with monitoring and site staff.


Thanks to Calyx’s flexible technology and excellent project management, the sponsor was able to recruit the number of patients they needed and dispense medication to those patients without putting their safety, or the trial, at risk.

The trial was a success and supported the sponsor’s request for Emergency Use Authorization from the FDA for the treatment of critically ill COVID-19 patients.


LG Chem was looking to outsource two trials, one of which had a complex randomization design: the randomization ratio was 10:10:10:10:1 and it included three stratification factors. Some strata targets were relatively low, increasing the risk of not fulfilling the allocation ratio within each possible stratum level. In addition, the protocol design stated that at month six, subjects randomized to the placebo arm would stop treatment and be considered as having completed the study.


A typical randomization design using a blocked list would require a very large block size of 41, which was not appropriate, considering the low recruitment target for some strata. Although a list could be built to meet those requirements, the sample size was not large enough to fill a block for each stratum, which would inevitably result in an imbalance of the allocation ratio at the end of the trial.
LG Chem was was also concerned that the population in a stratum may be too low to include all treatment arms. Without the right randomization design, LG Chem might need to increase recruitment targets, subsequently increasing study timelines and budget.

An additional challenge was the potential partial unblinding of the subjects within the same block as a placebo subject who reaches month six.


One of Calyx’s randomization experts was engaged in discussions with LG Chem during the IRT vendor selection process and throughout the study setup phase. They shared their point of view from a biostatistical aspect, and challenged the randomization design to confirm what could and could not be achieved through a blocked list.

They concluded that the risk of imbalance was too high to use a typical blocked list randomization approach and discussed the concept of minimization with LG Chem.

To highlight the relevance of a minimization design, they ran a simulation through a proprietary SAS program, which provided an idea of the balance across treatment arms and strata. The simulation was used to demonstrate the impact of various minimization algorithm parameters, helping the study team select the right settings in IRT.

For example, when using a dynamic randomization method such as minimization, the ICH-e9 advises that a random element is used within the process to make it non-deterministic. The size of the random element was investigated through simulation, along with the usual stratum weights, for the given populational characteristics of stratum recruitment. The random element is a powerful tool to reduce the predictability of assignment but can also affect the allocation ratio achievable with a small recruiting stratum group.

The simulation exercise provided a useful visualization of how minimization parameters would impact the study outcome, giving the LG Chem team confidence that the IRT randomization design would be suitable. The Calyx project team used Calyx’s own pre-validated parameterized minimization module for the ‘Pocock and Simon Method of Minimization with Biased Coin Assignment,’ which reduces the risk usually associated with ‘bespoke programming’ of such complex randomization methods. The module includes a full audit trail of each randomization event, allowing one to review why a patient was assigned to a specific treatment arm, and what calculations were done by the system.


The LG Chem study team trusted Calyx to meet the protocol’s complex randomization design, with adequate settings that ensured the right balance between treatment arms. Calyx’s expertise was a key component to the success of the trial setup.

“We relied heavily on Calyx’s IRT expertise and followed their recommendations for overcoming our studies’ challenges, resulting in a solution that perfectly met our RTSM needs for two complex gout trials.”

Book a meeting with Calyx randomization experts to learn more about minimization and other advanced RTSM algorithms to drive your trial’s success.


  • Phase III multi-center, double-blind, randomized, placebo-controlled study
  • 150 patients
  • 80+ sites across 25 countries


The sponsor was concerned that the following factors would cause a very high level of drug wastage:

  • Large number of sites
  • Clinical supplies team did not have the capacity to closely monitor sites


Calyx supply chain experts recommended Calyx IRT’s automated supply strategy management to:

  • Ensure each site is appropriately stocked by changing the IMP supply levels to match current recruitment rate
  • Take the burden off the clinical supplies manager
  • Minimize drug wastage
Identify which sites are active
Assess the site based on defined parameters
Switch the supply strategy, if needed
Generate shipment specific to the site’s needs

What is a supply strategy?

Supply strategies determine what stock is sent to a site; they combine site needs for both:


  • for randomization & for unplanned needs, for example, replacing damaged stock
  • quantities based on assumptions made at the start of the trial


  • typically, for subjects from randomization onwards
  • quantities based on subject treatment group, dosing regimen & visit schedule

Typical IRT approach

When the site is first activated, a strategy is selected based on the expected recruitment rate; this can be changed by the clinical supply manager via the IRT whenever the actual recruitment rate differs, so requires active monitoring

Advanced IRT approach

Automated supply strategy management differs as a site’s strategy is automatically changed to align with its actual recruitment rate.

The automated supply strategy management process:

For this study, Calyx IRT ensured that all subjects currently in screening could be randomized (and for this design received 1 kit of active or placebo).

The stock in the initial shipment was based on a dynamic count of subjects actively in screening at the site

The stock in the resupply shipments was based on a dynamic count of subjects actively in screening at the site

The quantities to ship per strategy were defined based on assumptions at the start of the trial with the support of the study’s dedicated Calyx IRT randomization and trial supply expert.  Clinical Supply Managers were able to amend those quantities through Calyx IRT self-service tools, once real-world patient and site data was available.

The Calyx IRT design also included:

  • The option for the clinical supplies team to easily override the automated supply strategy switch via our inventory management web system
  • Country-specific opt-outs for the automated process
  • Specific supply strategies for locations with a long lead time for shipments


By using Calyx IRT to automatically optimize the supply strategy for the site’s current recruitment situation, the sponsor was able to:

  • Relieve the clinical supply manager’s burden of closely monitoring each site’s supply levels against patient recruitment
  • Avoid failed patient visits due to insufficient medication
  • Ensure that each site is on the optimum supply strategy for their current recruitment rate
  • Keep drug wastage to a minimum

More on automated supply strategy management

Automated Supply Strategy Management is one of Calyx IRT’s advanced trial supply management options; it’s a flexible approach that can be tailored to specific protocol requirements and supply criteria.

The approach can be helpful for any type of study but is particularly useful at reducing the burden on the clinical supply manager when there are many sites.

Trials which benefit most

Large trials with many sites

Trials with a combination of local and central sourcing of IMP

  • The greater number of IMP types, the larger the possible combinations for resupply strategies
  • Automated switching would be used to assign a strategy based on the source settings for a site

Comparator trials where the comparator is known to be different based on patient characteristics

  • Automated switching could be used to monitor the level of comparator medication based on the number of patients with the required characteristic

Calyx RTSM expertise

Our dedicated IRT randomization and trial supply experts are here to help with any questions related to advanced trial supply management.

If you are interested in using automated supply strategy management, Calyx RTSM experts can assess the suitability of the method for your design considering:

  • Number of sites
  • Recruitment rates
  • Supply of future visits
  • Medication costs
  • Medication availability


  • Phase III study evaluating the efficacy of 2 different doses of active drug compared with placebo
  • 500 patients
  • 80+ sites participating across six countries
  • Site stratification


  • IMP was very expensive, so ensuring patient supply while limiting overage was critical
  • Storage of medication was limited for many sites


  • Calyx RTSM experts recommended “randomization prediction,” one of our advanced trial supply management options; this sends only the exact medication a site will use for upcoming patient randomization
  • Traditional IRT solutions base site stock levels on the expected number of randomizations and the amount of medication used for each treatment group
  • With randomization prediction, Calyx IRT sends medication to each site based on upcoming treatment group assignments known from the randomization list
  • Calyx RTSM experts worked with the sponsor to understand the expected site recruitment rates and recommended a range of supply strategies including low, medium, and high recruiter

We determined how many randomization list records’ worth of medication would be covered per supply strategy


  • Randomization prediction ensured a reduction in overage by removing the buffer
  • The stock required to be shipped to the site was reduced, as shipments contained specific medication for randomization assignments
  • Patients had the right drug at the right time

Randomization Prediction

Randomization prediction is one of Calyx IRT’s advanced trial supply management options; a flexible approach that can be tailored to specific protocol requirements & supply criteria.

If your study stratifies by site, Calyx IRT can send IMP to each site specific to upcoming treatment group assignments; this ensures overage is reduced.

Additional Features

— Can also cover medication needs for the next visit; beneficial if the next dosing visit is close to randomization

— Can be used with another stratification factor, in addition to site

— Does not require sites to have all types of medication for a randomization to proceed

— Can be combined with standard buffer & prediction strategies

  • Covering subsequent visits
  • Including buffer stock in case of IMP damage

Calyx RTSM Expertise

Our dedicated IRT randomization & trial supply experts are here to help with any questions related to advanced trial supply management.

If you are interested in using randomization prediction, Calyx RTSM experts can assess the suitability of the method for your design considering:

  • Recruitment rates
  • Supply of future visits
  • Blinding considerations
  • Randomization and statistical considerations

How does IRT help reduce drug wastage?

Interactive response technology (IRT) is a randomization and trial supply management (RTSM) tool that is often under-used when it comes to waste reduction. But when planned for early  during medication calculation and packaging planning – can be very effective at reducing drug waste and overall trial costs. 

Here we review eight approaches to drug waste reduction that can be implemented through a well-designed IRT system. Since the scenarios that determine the optimal approach will vary from trial to trial, it’s recommended that clinical trial sponsors and CROs work closely with their IRT provider to leverage their expertise and take full advantage of the system’s benefits. 

8 IRT Approaches That Help Reduce Drug Wastage

1) Prediction

The traditional way of supplying sites with medication is based on a buffer strategy, conventionally configured by trigger and resupply levels that can be adjusted per site. The IRT system generates a shipment request each time a resupply trigger is met for any of the pack types at site. It is common to replenish all pack types at the point a shipment request is raised to reduce overall shipment costs and to better maintain the blind within a shipment. The resupply level is a function of the recruitment rate, desired resupply frequency, site storage capacity, and study overage, and should be sufficient  to supply the anticipated number of unexpected events requiring medication until the next shipment should be raised.

Once a subject is randomized into the trial, he or she may require additional medication dispensations, which are usually supplied at further scheduled visits. As this schedule is known by the IRT, the system can calculate which packs are required by the subject and when, which means the needs of a continuing subject can be predicted.

The IRT system looks ahead over a defined time horizon (check range) and determines if the current stock on-site is sufficient to supply any visits in this window. If it is not, a new shipment of medication must be sent to supply the unfilled requirements. To reduce the number of shipments to sites, the system looks ahead over a longer time horizon (restock range) to decide if there are any additional scheduled visits for returning subjects that can be supplied at the same time. The restock range is determined by desired resupply frequency, site storage, and study overage, alongside other factors such as expected withdrawal rate.

Although these methods of resupplying sites significantly reduce the amount of wastage compared with the traditional system of supplying subject-numbered packs in a single shipment, there are times when these methods lack the sophistication and adaptability needed for more complex scenarios.

2) Fractional-prediction algorithm

In conventional dose-finding studies, where the sponsor investigates several doses of the same treatment, reducing drug wastage becomes more challenging.

Without an IRT system, the sponsor may need to maintain all dosing options within the protocol for all patients, subsequently leading to significant wastage, particularly if there are many doses. Although a prediction algorithm in IRT will provide significant savings, it may not sufficiently cover the potential for intra-patient dose titrations which may result in the need to maintain a higher stock of medication at each site to cover for that possibility.

Consider an example

Imagine there are four doses of an active compound (and matching placebo doses) and any subject can titrate one dose level at each visit. With a simple buffer strategy, the IRT system would need to keep a pack of each dose level for each treatment on site for every patient who could expect to titrate (based on the recruitment rate).

A more efficient option may be to predict all possible doses that each patient may need for each visit (e.g. for a subject on dose level 2 at the previous visit, the system could predict one of each of dose levels 1, 2, and 3 of the relevant treatment groups).

This method, while minimizing the irrelevant treatment types kept on site, still means that three packs are being sent to the investigative site when only one is expected to be used.

There is a type of predictive algorithm that means this potential wastage can further be reduced. Based on the approximate percentage titration rates, the IRT system can predict a fraction of a pack relative to that expectation. For example, if only 20% of patients on dose level 2 are expected to down-titrate to dose level 1, one fifth of a pack can be predicted for each patient currently on dose level 2.

When the needs are assessed by the IRT system, the fractions (for each of the pack types) are added and rounded up to the nearest whole. In our example, for every five subjects on dose level 2 of the same treatment, one pack of dose level 1 will be sent to the site.


With this approach, the fraction that is predicted is an important variable: setting the fraction too high can increase drug wastage, while setting it too low could result in dispensing failures. By using drug demand simulation, the fraction can be optimized by ensuring the risk of failures is low while keeping the minimum amount of medication at site.

3) Forcing

Forcing as part of trial design

Forcing is usually used as a method to avoid randomization failure due to unplanned lack of medication at site. There are occasions where forcing in IRT is more intentionally incorporated into the design to reduce drug wastage.

Forcing at site using a double randomization.

This method is suitable in trials where there are many treatments, scarce supplies and relatively low recruitment rates. In such situations, sending a full set of treatment supplies represents a substantial amount of wastage.

To reduce wastage, two separate randomization lists are employed. A first randomization list is prepared using the smallest block size, which will be the sum of the allocation ratios. For example, a block size of seven will be used for a trial of seven treatments with an equal allocation ratio. As sites are activated in IRT, they are sent supplies corresponding to a fraction of the block size. In our example, the first site activated may be sent treatments corresponding to the first three entries on the list. The second site activated would be sent the next three entries on the randomization list and so on. This means that a site could be sent two packs of the same treatment drawn from separate blocks.

Where that first randomization list is used to determine which treatments are shipped to sites, a second randomization list is used for the actual patient randomization. That list uses forcing to allocate the patient the next available randomization number corresponding to a treatment that is available at site. Randomization numbers corresponding to non-available treatments are skipped but are available for subsequent patients. As patients are recruited at a site, buffer stock resupplies are initiated using the first randomization list and prediction is used for future patient visits.

By forcing allocation from a balanced list, this technique will result in obtaining the best overall study treatment group balance in the face of limited supplies and a minimal wastage of medication. It is worth noting that this method is not recommended by ICH E9 Guidance. Arguably though, the protection against predictability and selection bias is increased by this method as, even if investigators know the block size and past treatment allocations, they cannot predict the medication for the next patient to be randomized.

4) Automated supply strategy management

Conventional stock management based on expected recruitment rates is limited because it requires active monitoring and action from the clinical supplies team to adapt to actual site-level recruitment.

Although this a valid solution for relatively small studies, it is not relevant to expect continuous close monitoring of all sites’ recruitment rate on a large global study.

An IRT system can help with adapting the stock needs based on usage rates, which is directly correlated to actual recruitment rate.

The stock at site is still categorized by low, medium and high recruitment, but the inventory management algorithm embedded in the IRT monitors the number of patients actively on-going treatment at each site and translates it into an amount of medication required over a pre-defined amount of time.

Advanced IRT systems include the ability to work on average usage as well as highest expected values, to increase the accuracy of the supply management. Should the number of active patients at site increase or decrease, the IRT system will automatically adapt the needs at site, resulting in medication stock being automatically managed.

Such strategies can also be automatically adapted to recruitment phases if medication requirements per phase vary. Clinical supplies managers will want to reduce the buffer stock at site to the minimum when all patients have been recruited for example, which can be automatically done by the IRT, saving the need to closely monitor recruitment phases.

Should the number of active patients at site increase or decrease, the IRT system will automatically adapt the needs at site, resulting in medication stock being automatically managed. 

5) Randomization prediction

Buffer stock defined in terms of trigger and resupply or usage rates are determined in terms of the number of packs that may be required over a specified period, based on an expected recruitment rate and the likely treatment allocations. Supplying medication in this way will inevitably lead to wastage as the number of packs that need to be maintained to randomize a set number of patients will always exceed their actual needs.

In a study with three treatment groups where each group receives a different medication type, to enable the randomization of two patients within a short period, two packs of each medication type must be kept on-site.

In clinical trials where the randomization code is stratified at a site level, there is a method of predicting the buffer needed by using a strategy known as randomization prediction.

The medication needs of a site are determined by the randomization schedule; by looking forward in the schedule, the IRT system forecasts future treatment allocations. Supply orders are generated for sites when the inventory levels fall below or equal to that required to randomize the next X patients to the treatments listed in the randomization schedule, where X is determined by the highest expected recruitment in the check range.

The resupply amounts are similarly determined by the randomization schedule, and shipments will contain enough medication to enable the site to randomize the next Y patients. Similar to normal buffer, Y is determined by the average anticipated recruitment rate in the restock range.

Using normal buffer, in the example above six medication packs would need to be maintained to randomize two subjects; if the randomization scheme was stratified by site and the method of randomization prediction used to maintain buffer stock for randomizations, only two packs would need to be kept onsite to enable randomization of the two patients.


6) Blinded group ordering

In many studies it is often the case that recruitment is slow, or very few subjects are expected to be recruited at each site. In these situations where the trial design is double blind, it is very likely that a shipment would contain medication for a single patient and require that a random pack is added to the shipment so that it remains blinded. This additional pack may result in a relatively large amount of wastage when considering it across a full study, particularly where there are several treatment groups or packaging types.

An alternative method of supplying buffer medication, known as blinded group ordering, can be used in this instance to reduce this wastage.

The principle behind blinded group ordering is that the initial supply to site contains the random blinding medication, rather than having to add it to each consignment. It is like a regular buffer strategy; however, the blind group ordering strategy also considers maintaining a total buffer based on an overall quantity.

For example, consider a study with two pack types, active and placebo: the blind group ordering strategy could be set to maintain one pack of active, one pack of placebo, and three packs overall. The third pack would be randomly selected (i.e. either active or placebo). Let’s assume the current stock at a site is 1 × A and 2 × P (where the second placebo is the random pack).

When the next patient is randomized, one of the following scenarios could occur:

  • If the patient is randomized to active, a single pack shipment would be raised containing one pack of active (as there would be no active packs left at the site).
  • If the patient is randomized to placebo, a single pack shipment would be raised containing either a pack of active or a pack of placebo (as there would still be a pack of placebo remaining at the site, the IRT system would replace the random pack).

Using such a method means that single pack shipments are not partially unblinding as the investigator does not know whether the pack in the shipment is replacing the pack they have just dispensed or whether it is a random pack. This can therefore reduce the amount of drug wastage that would otherwise result from adding a significant number of packs to blind each shipment.

7) Pack substitution (for open label studies vs double-blind)

Pack substitution in a clinical trial is usually used when a medication dispensation plan allows for multiple dosing formulations of pack combinations to be used to constitute the same overall dose. For example, a 100mg dose may be made up from 4 x 25mg tablets, 2 x 50mg or 1 x 100 mg.  The decision about how to provide this dose will be made to optimize multiple different objectives, which could relate to frequency of use by the patient, ease of use for the patient, availability, or a separate clinical consideration.

In a situation where the formulation of a drug is changed during a trial, there is a high risk that switching from a formulation to the other results in a lot of wastage. If the existing packs are replaced before they are fully used, all remaining packs will be wasted. It could result in waste at both site and depot level.

Pack substitution is a good solution to reduce wastage in such a situation. The switch from the old formulation to the new formulation is done on a site needs basis, with the IRT shipping the old formulation to sites as a priority and only shipping the new formulation to sites once the depot has run out of the old formulation.

From a medication dispensing point of view, the IRT system also selects the old formulation as a priority and only starts dispensing the new formulation when there is not enough of the old one for a full dispensing.

It is crucial to plan pack substitution well in a scenario like this, as the expiry date of the new formulation needs to be long enough to account for the relatively slow replacement of the old formulation. Sponsors will also want to share this process with depots and sites, so they are prepared for the new formulation replacing the old only when all the old formulation has been used.

8) Medication Pooling

A good way to reduce drug wastage is to look at harmonizing the use of medication across a program or suite of studies, which is referred to as medication pooling. Sponsors should not only achieve a more efficient usage of medication, they can also reduce study-specific supply management oversight. A well-designed IRT solution will be a critical tool to manage medication pooling correctly and help realize costs efficiencies.

Medication pooling in IRT is not new, but many study teams are still unclear about what pooling can and cannot do, and how the regulators will react to its use. Medication pooling will not apply to all programs, as there is a need to have enough overlap in same pack type requirements across protocols for it to be beneficial. Sponsors also need to consider the impact on labelling and should refer to regulations in force across all countries included in each trial, to make sure they follow the right recommendations. Having multiple labels on packs can become confusing to depots and sites, specific labelling solutions are likely to be required to reduce both confusion and risk of error.

Let’s define medication pooling

Pooling is possible when more than one protocols operating at the same depot and/or clinical sites uses the same medication. Pooling allows medication supplies to be shared across multiple trials, hence medication for each protocol is indistinguishable in content, count, and in some cases labelling.

It is common to consider medication pooling from a medication wastage reduction point of view only, but it is also an effective solution to address availability (scarcity) concerns or other restrictions that affect the supply of medication.

When considering sharing medication across trials, medication pooling can be applied at depot level or at site level; we will review both options in this document.

Depot vs site level pooling

Depot and site level pooling have different supply chain and IRT set up considerations. The level where the pooling takes place will impact on packs labelling, depot supply management, as well as site supply management. Both levels also represent different potential savings, site pooling offering the highest medication savings.

In simple terms, depot medication pooling consists in managing medication across trials at depot but having protocol-specific medication at site (even if the pack types are identical). Site medication pooling on the other hand results in managing medication across trials both at depot and site levels. The latter solution is always felt to be the best approach, however determining the size of the one pooled stock for all events in the group of studies could be complex.

Site level pooling

Although not strictly qualified as site level medication pooling, a simple way to reduce medication wastage is to add an extension protocol to the IRT designed for the existing protocol. This is common practice within the industry, and it allows to use packs across both protocols without much impact to the IRT design. This approach is limited to the combination of two protocols though.

Site level pooling associated with a program of studies allows a site to have one supply of stock for all the protocols it is recruiting into within the ‘pooling group’ of protocols.  The site has one supply scheme across all the protocols, rather than a separate supply scheme per protocol.  In an open label trial, it helps avoid any compliance issues where sites may run out of stock for one protocol, but have the needed medication available for another protocol.

What is the impact on site supply strategies?

Depot level pooling offers the flexibility to include both pooled pack types and medication that is not pooled, where a certain pack type is assigned to a specific protocol for example.

For site level pooling in the other hand, protocol specific packs should be restricted to medication solely used within a particular protocol for a purpose unique to that design. Sites will have one supply of medication, shipments will be raised without distinction between protocols.

Medication pooling can be used in combination with predictive supply for upcoming patient visits within a given site for all the studies in the program represented at the site.  Additional buffer stock should be held to cover new patients and unscheduled resupplies. It is very important to assess needs across all protocols to determine a buffer stock that can meet the needs of the trials currently active within the site. Sponsors will also need to consider how buffer stock should be impacted if a new protocol is added to the pooling, and how to implement the right level of flexibility in supply strategies. The definition of supply strategies becomes more complex if protocols include both pooled medication and protocol-specific pack types.


Sarah Waters
Calyx, Nottingham, UK

Iain Dowlman
Calyx, Nottingham, UK

Kevin Drake
Calyx, Nottingham, UK

Lee Gamble
Calyx, Nottingham, UK

Martin Lang
Calyx, Nottingham, UK

Damian McEntegart
Calyx, Nottingham, UK

Damian McEntegart
Calyx, Nottingham, UK

Learn how IRT consulting and design from Calyx can support platform trials to accelerate the clinical development lifecycle.

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