Elucidating Opportunities for Cost Reduction of Clinical Trials Through Supply Management Optimization

23rd October 2018 (Last Updated December 3rd, 2018 10:01)

Anthony Zuccarello, Associate Director, IRT and Global Clinical Supply Strategy, Amicus Therapeutics, offers insight on how to create efficiencies within the clinical supply chain

Elucidating Opportunities for Cost Reduction of Clinical Trials Through Supply Management Optimization

Given the volatility of the global financial climate, an increasing number of companies are adopting a culture supporting a higher level of cost sensitivity than ever before. This is especially true for health care, a sector that experiences among the largest economic fluctuations of any, with companies reporting an average change in stock price as high as 4.4 percent in a single day upon reporting quarterly earnings[1]. Within this sector, this issue is exacerbated in both the pharmaceutical and biotech industries by the constant threat of impending expiry of patents for their products on the horizon. That said, if companies are to survive in this harsh economic environment, they need to operate as efficiently as possible at all times.

As one of the largest cost drivers in these industries is the conduct of clinical trials by sponsor companies (which can cost upwards of $79.1 million to move from phase I to phase III, and as high as $52.9 million for a single phase III study[2]), focusing on ways to manage costs and reduce waste is of paramount importance. Fortunately, solving issues that lead to decreased efficiency during the clinical trial phase is a bit of a Gordian knot in that it may seem much more difficult to do than it actually is in practice. In fact, there are several items that could be considered low hanging fruit that are common to nearly all clinical trials, such as enhancing supply chain management through analyzing costs to determine best practice.

Don’t Limit Decision Making to Previous Actions

As a primary, though often neglected example, consider the routes in which the product is shipped. Very often, clinical trials operate at the international level, especially when a product is being tested in phase III or IV as a large sample size of patients is necessary. Many times, international shipping routes are setup based on past experience rather than on feasibility exercise. For example, if a sponsor is conducting a study in which Australia is to be a participating country, the decision to either open a local depot or to ship direct to clinical site from a main depot needs to be made. When making such a decision, the sponsor should not limit decision making to what has been done on prior studies, as each trial has a unique set of circumstances. Instead, the demand within the country (in this case Australia), needs to be projected and used to calculate a total number of anticipated shipments. At that point, each of the scenarios that follow should have costs calculated, with bottom lines being compared side by side:

  1. The cost for individual shipments from the main depot to the clinical site
  2. The combined cost to:
    • Ship a bulk from the main depot to the local depot
    • Ship individual shipments from the local depot to the site
    • Costs to initiate and maintain the local depot

Whether or not option one or two above is more financially advantageous will depend on a number of factors, and will change based on the distinctive conditions of each study, but the point is that even doing a simple cost analysis exercise such as this can potentially save hundreds of thousands of dollars per study… and this is only a single example.

While IRT Simplifies Processes, it doesn’t Eliminate Need for Manual Oversight

Another method to significantly lower expenses that requires a relatively small investment is to employ automation. IRT (interactive response technology) is a fairly common way to automate several clinical trial operations through use of software. IRT is particularly impactful in the management of supplies as it can be used to achieve reduction of manual oversight. IRT can be programmed to automatically supply sites and maintain inventory levels by use of static and dynamic variables in conjunction with time boxed analytics. Static variables can be used to trigger shipments to resupply a clinical site when inventory falls to a certain level. For example, it can be programmed to resupply a site back up to 10 units, when the inventory has dropped to five units.

Dynamic variables can be used to anticipate patient visits over a specified period of time and ensure that there are adequate supplies on site during visit windows. For instance, if programmed to anticipate demand over a 30-day period, assuming that two patients have visits scheduled within that time – each needing four kits, the system will automatically request a shipment for eight units, in addition to what is required by the static values mentioned previously. While relatively simplistic, this saves a significant amount of manpower, and the associated expense, that would be needed to do this work manually, especially if dealing with a complex trial, or one employing a large number of patients/sites.

While IRT does not completely eliminate the need for manual oversight, there are mechanisms in place to minimize the small amount of supervision that is required while simultaneously reducing the risk of stock outages, such as programmable alerts. The IRT system can be designed to constantly monitor mundane items including lot expiries and depot inventories, and send alerts to supply managers when specified milestones have been reached. For instance, alerts can be sent when depot inventories fall to “X” units, or when a lot is within “Y’ days of expiring. Such notifications prompt the supplier manager that a depot shipment should be raised or that another batch of material should be planned for manufacturing, helping to ensure smooth operation of the trial.

IRT can Control Shipments

It is important to consider that as IRT is a software-based application, the uses and capabilities are limited only to the imagination of the designer and end user. That said, a system can be built to predict the fastest and/or most cost-effective pathways for shipments based on real-time data.

Similar to the way GPS calculates the best route for travel considering traffic conditions and tolls, if integrated with the database of a courier (such as DHL), average shipping times to get material into specified countries could be weighed against shipping costs to determine the most efficient and cost-effective route of transport. While savings per shipment might seem modest when compared to the overall cost of the trial, the aggregated savings can amass to hundreds of thousands of dollars, especially if the trial has a large number of sites across the globe.

A similar automation capability that should also be noted is that of prioritization of shipping through labeling factors, such as expiry and country-specific requirements. IRT can be designed to control shipments so that older lots are prioritized over those that are younger, and that once a certain threshold has been reached, shipping stops entirely for a particular batch. It can also be programmed to segregate label types, so that material that may have been split into batches with different labels can be shipped appropriately.

Opportunities to Recover Costs and Increase Efficiencies

In closing, it should be noted that there are numerous opportunities to recover costs and increase efficiencies in clinical trials through tighter management of the supply chain, of which the preceding examples are just a few. In addition to their explicit direct cost savings, many of these functional capabilities can have extraordinary implicit savings by preventing clinical trial delays due to supply-related issues. Such delays create major opportunity costs associated with lost sales due to shortened windows to sell product prior to expiry of patent, which can equate to millions of dollars and should therefore be given a high level of consideration.



[1] https://www.investopedia.com/financial-edge/0712/the-8-most-volatile-sectors.aspx

[2] https://www.ncbi.nlm.nih.gov/m/pubmed/26908540/