Statistical Sampling of In-Transit Pharmaceuticals Keeps the Supply Chain Moving

Following widespread concerns about counterfeit pharmaceuticals in the early 2000s, the FDA and individual states began reconsidering the design of the pharmaceutical supply chain.

The effort to keep counterfeit medications out of the United States supply chain is an important priority for pharmacists.

In July 2003, the FDA formed the Counterfeit Drug Task Force to develop a framework for securing the pharmaceutical supply chain. One important part of this framework is the use of product authentication technologies, or e-pedigrees. In California, the e-pedigree requirement would have taken effect between January 1, 2015 and July 1, 2017, had it not been preempted by the federal government.

On November 27, 2013, US President Barack Obama codified Public Law 113-54. This law disables states from enacting e-pedigree systems, but includes provisions for a national tracking and tracing system for prescription medications through a series of requirements for manufacturers that are enforced federally.

One reason for rejecting the e-pedigree requirement may be the overly stringent requirements of the California law, which required nearly universal scanning of all pharmaceuticals at several supply chain checkpoints. In a 2012 whitepaper published by the Stanford Graduate School of Business, Barchi Gillai, PhD, outlined the benefits of statistical sampling to maximize the quality of the pharmaceutical supply chain without disrupting the trade to an unreasonable degree.

Statistical sampling is an alternative to scanning all pharmaceuticals in the supply chain. This resource-saving alternative to 100% tracking was first adopted by the US military in 1963, and was later recognized by the American National Standards Institute in 1971. Sampling systems of this design require random sampling rather than complete tracking, from the point of origin at the manufacturer to the pharmacy.

In determining the amount of random sampling to be carried out, consumer risk, manufacturer risk, and other factors must be accounted for in a statistical model. Additionally, the model must account for changing conditions. When no counterfeiting has been detected, a more relaxed procedure may be used, while a more stringent product quality check can be instituted if counterfeiting has been detected in the supply chain.

Using this system, Gillai calculated that less than 5% of the stock in transit would require scanning and inspection to keep the risk of a counterfeit item passing inspection well under 1%. In this way, statistical sampling can help reduce disruptions to trade, while keeping quality high without unduly increasing the cost and inconvenience of shipping.

Although it is clear that some substances (eg, schedule II narcotics) must be scanned and tracked at a 100% rate, the shipping of many high-volume pharmaceuticals may be impracticable without statistical sampling techniques. Statistical sampling in the product supply chain has been used for over 50 years, and helps safeguard product quality while ensuring consumer access to pharmaceuticals.

References

• Gillai B. Stanford Graduate School of Business. The Pharmaceutical Supply Chain: Improved Security through Statistical Sampling. https://www.gsb.stanford.edu/sites/default/files/documents/Pharmaceutical_Supply_Chain_Security.pdf. Accessed February 2015.
• California Board of Pharmacy. California's E-Pedigree Law Preempted. http://www.pharmacy.ca.gov/about/e_pedigree_law_preempted.shtml. Accessed February 2015.
• Public Law 113-54. http://www.gpo.gov/fdsys/pkg/PLAW-113publ54/pdf/PLAW-113publ54.pdf. Accessed February 2015.