Analyze This!

The 450-page Pharmaceutical Manufacturing benchmarking study released in October offers a gold mine of information on how to improve, for those industry professionals and regulators brave enough to dive into the data. In this feature, Jeff Macher and Jackson Nickerson discuss challenges they faced, study results, a major caveat and future plans.

By Agnes Shanley, Editor in Chief

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  Jeffrey Macher

Jackson Nickerson

In October, data from the first Pharmaceutical Research Manufacturing Project, over 450 pages of it (to download, click here), were finally released. Led by Georgetown University (Washington, D.C.) professor Jeffrey Macher and Washington University (St. Louis, Mo.) professor Jackson Nickerson, the project took four years.

“I thought we’d be done in six months,” says Macher, but it took time —“handholding, interviews and meetings,“ he says — to build the level of trust required to get FDA and manufacturers to participate. “The enormity of data we were asking for probably scared some manufacturers off,” he quips. The fact that the research was completely independent, and funded entirely by Georgetown and Washington Universities may have convinced more pharmaceutical firms to participate, while some stiff nondisclosure agreements didn’t hurt.

  Jackson Nickerson

Jeffrey Macher

The study was released with the attention-getting press release headline “Drug manufacturing wastes $50 billion a year.” If that number isn’t believable, Nickerson and Macher say it’s extremely conservative.

Early in the survey process, back in 2001-2002, both professors surveyed more than 50 senior pharma executives, FDA officials, and senior managers at biopharma and vendor companies. “To the one, they agreed,” Nickerson says, “that if FDA could change the way it regulated, and if managers could respond to a change in the way it regulated, the industry could save anywhere from 10 to 50% of the cost of goods sold,” he says. Some interviewees suggested more than 50%, but the professors took 15% to come up with the figure (MIT had pegged it earlier at 25%).

The study’s first phase, completed last year, focused on FDA (see "Benchmarking FDA Site Selection and Inspectors" below), cGMP inspection data from 1990 to 2003; results indicate that the Agency’s risk management approach, articulated in 21st Century GMPs, is exactly what the Agency and the industry need right now. Their research also concludes that the Agency’s Inspectorate Training program will soon show results, if it hasn’t already. “We suggest that FDA move more rapidly toward risk-based site selection for its inspections,” Nickerson says. The professors are now moving on to Phase II of the FDA study.


Figure 1. Click here for larger image.
Figure 1: Correlation Between Factors and Batches Failed, Yield and Cycle Time

But, if FDA is a secretive organization, it was even more challenging to gain access to the inner sanctum of biopharma and pharma facilities. Information gathered from 19 manufacturers representing 42 facilities in the U.S. and Europe, from 1999 to 2003, a mix of large and small firms, some vertically integrated as well as a few contract manufacturers.

Information contained in hundreds of pages of graphs and analyses can be distilled to the following messages:

    • The extent and use of information technology (IT) at any facility has a direct impact on the frequency of lot failures and other problems. In general, Nickerson says, having a centralized database accessible to all, and people trained to use it, improves performance.

    • Degree of manufacturing complexity tends to have a negative impact on overall performance, thus contract manufacturing operations tend to show inferior manufacturing performance because they often deal with more complex scale and scope issues, Nickerson says.

    • Operational decisions are best made by those doing the work, so the lower the point of decisionmaking, the better the results; facilities where the plant floor operations staff make the day-to-day manufacturing choices generally face fewer ruined batches and other manufacturing problems than facilities where such decisions come from on high.

    • Bringing people from outside any given functional area improves performance, particularly when managing deviation.

  • New approaches, such as applying process analytical technologies (PAT), are initially disruptive but are likely to accelerate the learning curve. Techniques such as PAT can increase accelerate learning, but the data show that they do not improve performance, at least not at first.

The FDA portion of the study also offers many insights for manufacturers, and suggests that the way downsizings, mergers and acquisitions are handled today may hurt regulatory performance. Consider the impact of mergers and acquisitions, an almost monthly occurrence in this industry. After any merger, Nickerson says, nothing happens to the manufacturing facility at first. But then, after two or three years the “grey hairs” retire or are fired, and new operating procedures, roles and responsibilities, and suppliers are brought in. When these changes finally do occur, the company’s likelihood of being in violation of FDA regulations (or receiving an ordered action issue (OAI) increases significantly.

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