According to findings of the largest empirical study ever performed on the interplay of pharmaceutical manufacturing and the Food and Drug Administration (FDA), the pharmaceutical industry is wasting more than $50 billion a year in manufacturing costs—costs that could be better applied to lower prices or increased research and development.  The study, conducted jointly by Olin School of Business at Washington University and McDonough School of Business at Georgetown University, received no funding from either the pharmaceutical industry or the FDA.

The goal of the study was to understand how the FDA regulates pharmaceutical production and to see where there may be conflicts that inhibit advances in manufacturing.  The researchers collected data from 42 manufacturing facilities owned by 19 manufacturers.  They studied the companies’ manufacturing performance in terms of cycle time, frequency of deviations, reasons for deviations, yield, and improvement rates on key manufacturing metrics.

The FDA, to its credit, has openly acknowledged that historical compliance prescriptions have had unintended side effects—namely, discouraging manufacturers from embracing new technologies and process improvements that address the findings above. The costs and risks associated with change, as a consequence of stringent validation and regulatory re-filing requirements, have been perceived by manufacturers as being too high.  In response, the FDA has recently instituted sweeping changes that are transforming the way life science companies look at their research, development and manufacturing process engineering organizations.  Central to these changes is the overarching Quality-by-Design paradigm, a centerpiece of the FDA’s “Pharmaceutical Quality for the 21^st Century—A Risk-Based Approach.”

However, despite the bad news regarding manufacturing inefficiency, the study did identify two positive factors that temper manufacturing inefficiency: lending support for implementation of Quality-by-Design initiatives by using a comprehensive solution like Inference for QbD.  Specifically,

·      Application of information technology correlated with superior manufacturing metrics.  Companies that employed information technology to electronically track and report on manufacturing (people, processes and deviations) and centrally stored all their data, uniformly displayed superior manufacturing performance relative to those not using such information technology.

·      Driving decision-making down the ranks results in higher overall manufacturing performance.  This important goal is supported by Inference for QbD. Increased capacity for employees in lower ranks to make decisions directly correlates to gains in manufacturing performance. This is especially true when considering matters related to deviation management, lot failure, lot review and process validation.