This is the first of a five blog series which reviews the industry and business impact mass serialisation is having within the pharmaceutical supply chain, covering topics such as implementation, business opportunity, labeling and the industry stakeholders.
Safeguarding the manufacture and delivery of medicines has always been a key priority for the pharmaceutical industry, but changes are afoot to enshrine this responsibility in law. Is your business ready to meet impending regulations?
The pharma industry is characterized by highly complex supply chains between the manufacturer and end consumer. Over the counter drugs are produced, distributed, repackaged and sold on by a series of entities before reaching the patient, introducing the potential for criminal activity.
Globally, pharma companies lose around $75 billion every year to counterfeit, grey market and stolen product. Counterfeit drugs pose patient safety risks: they may be contaminated, contain the wrong ingredient or no active ingredient at all, or contain the right active ingredient but at the wrong dose. Other illegal schemes include social reimbursement fraud, whereby false claims are submitted to insurers or programmes such as Medicare for financial gain, with the latter hitting governments and taxpayers in the pocket.
Navigating the evolving regulatory landscape
Serialisation aims to combat these illicit activities by tracking and tracing the passage of prescription drugs through the supply chain from manufacturing to dispensing, typically through automated, electronic means. Serialisation is already a legal requirement in several countries, such as Turkey, Brazil, South Korea and China, but in this post, we’ll be specifically focusing on the impact of two major pan-regional regulations:
In the US, the Drug Supply Chain Security Act (DSCSA) was initially introduced in 2014, and takes a phased approach. It requires the adoption of lot-level product traceability, as well as the ability to produce transaction information, history and statements at 48 hours’ notice. Manufacturers have been required to put a unique identifier (National Drug Code and serial number) on drug packages since November 2017, while repackagers have been given until 27thNovember 2018 to comply. Following serialisation, products will be need to be verified at the package level by manufacturers from 27thNovember 2017, repackagers from the same date in 2018, wholesale distributors from 2019 and dispensers from 2020.
In Europe, the EU Falsified Medicines Directive (FMD) is due to come into force on 9thFebruary 2019, and will affect manufacturers, wholesalers, distributors and pharmacists. The legislation will require all parties to be able to verify the authenticity of a medicinal product, identify individual packs, and determine whether the outer packaging has been tampered with. The FMD mandates the use of a tamper-proof security seal and a 2D barcode, serialised with a unique, randomised number.
Establishing a standard to mitigate complexity
As pharmaceutical supply chains are complex and global by nature, the industry is looking to standardise on a common identification system. GS1 is a globally recognised and endorsed standard, and is based on a foundation of unique identification keys (Global Trade Item Number). It enables product attributes to be recorded, including batch or lot number, expiry date and unique serial number, and is visualised as a GS1 DataMatrix 2D barcode, which enables a substantial amount of data to be encoded in a small physical space.
With the EU FMD compliance deadline looming large, businesses can’t afford to focus myopically on putting a barcode on a bottle or a box. The challenge of item-level serialisation extends beyond the confines of the packaging hall, and implementing serialisation across the enterprise will involve both digital disruption and business transformation. However, it’s also an opportunity to achieve substantial operational gains: combating ineffective product recalls and returns, increasing supply chain efficiency and visibility, and providing a data foundation for analytical tools to predict individual patient behavior and drive innovation.
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