Preventing drug counterfeiting


Flask Data provides a one-stop cloud subscription for EDC, data management and statistics.

Counterfeiting is old as money itself.

We recently had the opportunity to work with a large generic pharmaceutical company examining innovative methods for preventing product counterfeiting. In order to build cost justification for the project, we performed a quantitative threat modeling exercise that involved valuation of assets and analysis of a number of product counterfeiting threat scenarios.

The threat model is available on request – please contact us and we will be happy to send you a copy of the threat model and an explanation of how to use it in your own product counterfeiting scenarios.

The first question to be asked – what is the volume of financial damage due to drug counterfeiting?

From a pharma perspective –  economic damage due to drug counterfeiting

There is a dearth of scientific method in estimates of worldwide economic damage due to counterfeiting (7 percent of world trade seems to be extent of the mathematical model; as a result – the numbers range wildly from 10Billion dollars/year to 600 Billion Dollars/year).The OECD reports that The economic impact of counterfeiting to the pharmaceutical industry is USD 17 billion/year.

This level of damage has generated a huge business in anti-counterfeiting technologies. There are hundreds of products and methods from RFID tagging to nano-particles that are being proposed as solutions (not even risk mitigation) to the threat of drug counterfeiting. Most of these technologies are not cost-effective and can be easily sidestepped (to make a fake product, a counterfeiter only has to make it look real – he doesn’t have to do the original research, development and manufacturing process).

From a consumer perspective – Public Health implications

Not surprisingly, because of the public health implications (how many men die from fake Viagra or women die from fake silicon breast implants), regulators like the FDA are stepping in. California is setting the gold standard like they did with consumer privacy protection – this time with a bill that would require a drug “e-Pedigree”.

e-Pedigreetrying to combat the problem with supply chain traceability

The California e-Pedigree law (SB 1370) specifies pharmaceutical product serialization which “require the pedigree to contain the drug’s unique identification number established at the point of drug manufacture.”

When used through the supply chain, the e-Pedigree will help track and trace product, identify counterfeiters and enable consumers to authenticate the products they buy at the point of sale.

It’s impossible for me to estimate how much e-Pedigree will eventually cost (it only becomes mandatory in California in 2011) but it’s pretty clear that with all the packaging and information technology it’s going to be a pretty steep price for the drug supply chain.

Modeling threats to consumers

Instead of trying to solve an impossible problem, I decided to model a subset of the e-Pedigree. i.e. use of product serialization at the point of sale to the consumer in a pharmacy.

The notion of having consumers call in a numeric token at the point of sale, in order to authenticate a drug they purchased, was first proposed by Johnston, a researcher at Los Alamos Laboratories. There are commercial implementations, available from companies like Dintag and Algoril and Verify Brand. Dintag in particular appears to have the most complete implementation.

How does a call-in numeric token work?

Pharmaceutical end user customers would be able to authenticate the validity of a random, ID number printed on packages via a simple Web search query similar to the Google search page. Other channels might also be used – for example: sending a text message with a cell phone or a making a phone call to an automated voice response service. Customers with cell phone cameras could send a picture of the label over the Web to the system, which would extract the ID number using OCR and return a text message to the consumer.

When used by consumers at the point of sale, or at home; product serialization becomes a relatively low-cost, low-tech way to authenticate a product, since there is no supply chain integration required and the large number of consumer eyeballs calling in tokens is free to the manufacturer. I believe that there is also a viral marketing effect as people tell their friends about the system.

A Threat analysis of a drug counterfeiting countermeasure

We performed a threat analysis of the call-in numeric token method. Feel free to contact us and we will be happy to send you a copy of the threat model with an explanation of how to use it for your analyzing product counterfeiting scenarios. The threat analysis of the call in numeric token system was performed using the PTA (Practical Threat Analysis) methodology . The analysis shows that by implementing the right security countermeasures, we can effectively mitigate risk and ensure confidentiality, availability and integrity of system assets.

Threats are denoted (T-XX) and corresponding security countemeasures are denoted (C-XX)

T1 – Attackers may attempt to guess tokens
C01 – Make it difficult to guess by using a large, unpredictable collection of numbers; assure that there are at least 1000x more possible serial numbers than in a typical lot.
C02- Require additional data in the authentication query, for example, lot number.
C03 – Throttle multiple requests from a particular IP address
C16 – Block requests from known spammers using blocklists such as spamhaus.

T2 – Malicious outsiders may attempt to obtain tokens and reuse them on counterfeited products
C02 – Require additional data in the authentication query, for example, lot number.
C04 – The cost of buying legitimate products for the tokens is an economic disincentive
C10 – Use tamper-evident packaging, don’t print the token on external package.
C11 – Use query threshold of 3-4 that will prevent attackers from recycling stolen tokens

T3 – Trusted insiders may collude with criminals or competitors to obtain bottle numbers
C05 – Use a diskless interface box to feed the variable printer
C06 – Limit internal network access to the token database
C09 – Enforce strong passwords for system maintainers and recycle regularly
C15 – Require supply chain partners to sign NDA

T4 – Attackers may attempt to cause the service to become unavailable with a DDOS attack
C03 – Throttle multiple requests from a particular IP address
C08 – Do proactive patch management on token query server operating systems
C12 – Use commercial DDOS appliance above a certain query volume

T5 – Attackers may attempt to discredit the token query service with spoofing and/or phishing
C16 – Block requests from known spammers using blocklists such as spamhaus.
C17 – Operate the Web search site under the manfucturer’s corporate domain
C18 – Use HTTPS and provide a valid digital certificate for token query server
C19 – Provide branded and digitally signed widget for iGoogle
C20 – Provide consumer protection policy on query Web site
C01 – Make it difficult to guess by using a large, unpredictable collection of numbers, assuring that there are at least 1000x more tokens than in a typical lot size.

Related Posts Plugin for WordPress, Blogger...

Flask Data is a technology company with a strong people focus. We are a diverse group of computer scientists and clinical operations specialists based in Israel, the US and India. We are accomplished at providing our customers with the most effective way to achieve high quality clinical data and assure patient safety. There is no single solution that works for every clinical trial. We work hard to understand your unique situation. We work with your team to develop the best solution to achieve high quality clinical data and assure patient safety the same day you engage with patients.

Flask Data – same data data and safety solutions for clinical trials.

Contact us to learn more

Tell your friends and colleagues about us. Thanks!
Share this