Patent protected, blockbuster drugs, large sales, high margins may soon be a thing of the past for many Pharma companies. Already, generic drugs account for 85% of the prescriptions written in the U.S. each year. According to The Economist, drugs that account for $170 billion in annual revenues will be off-patent protection by 2015 and will be open to competition from generic versions.
To remain competitive in these challenging times; cutting down production costs is no longer just an option for drug makers, but a necessity. Moving manufacturing to emerging economies with lower labour costs seems to be a sound solution on the face of it. But companies have found to their chagrin that the costs involved in maintaining product quality sometimes surpass the benefits of lower costs.
Other than manufacturing costs, Pharma companies have to consider quality and compliance challenges. A large pharmaceutical company lost sales worth $900 million in one year due to recalls. And this is not an isolated case; there were 419 FDA quality recalls in 2011 alone. The new set of regulatory guidelines issued in 2011 that stresses on process understanding and calls for improvement on a continuous basis, has made compliance even more challenging.
How should drug-making companies meet these complex challenges? Well, they need not look very far. They must harness the power of the information they already have: focus on manufacturing analytics.
Consider the current scenario in most companies: The ERP System collects raw material data while the Manufacturing Execution System (MES) contains the details of specific batch manufacturing execution. Data historian software stores operating parameters. The Laboratory Information Management System (LIMS) captures product quality data while the Incident Management System captures adverse patient events. As you can see, data is maintained in silos and it is quite impossible to do anything with these fragmented bits of information.
To improve process, yield and compliance, all the data needs to be consolidated and viewed in its entirety. Techniques like Multivariate Analysis (MVA) can be used to help identify Critical Process Parameters (CPPs). These parameters can be used to build design space as required by regulatory initiatives like Quality by Design (QbD). Real-time measurement of CPPs translates into fewer instances of product failure, reduced need for product rework and higher yield. Data consolidation will also enable display of key operational quality metrics that will facilitate quicker decision-making.
No wonder then, that an increasing number of Pharma companies are looking for manufacturing analytics solutions to improve production efficiency, quality and compliance. Given the benefits, it is no surprise that pharmaceutical companies are turning to end-to-end solutions that provide a holistic view of manufacturing, quality and patient event data enabling process improvement and cost reduction while simultaneously maintaining manufacturing compliance and product quality.