In some respects, the life sciences sector is unique among industries. Every move pharmaceutical and biotech manufacturers make is tightly monitored by the U.S. Food and Drug Administration (FDA), and the quality of the end product is literally a matter of life and death. So, it's no surprise that these manufacturers do everything possible to ensure the highest quality of their end product at all times.

But quality comes at an extremely high cost. Goods are laboriously tested before being released to the market, resulting in an industry average for rework and scrap rates of 50%, according to AMR Research (Boston). The costs of this post-facto approach to quality control are high -- a single scrapped batch can cost a company as much as $3 million to $4 million, according to AMR.

These error rates, which are much higher than those in other process industries such as oil & gas and food & beverage, mean the entire industry suffers not only in terms of cost but in protracted cycle times. Pharma cycle times typically run 30 to 90 days, according to AMR, but when processes go awry, that time can double.

"Big pharma has had the luxury of huge margins," says Alison Smith, senior research analyst at AMR. "The cost of raw materials is small. Unlike virtually every other industry, they have been able to operate remarkably inefficiently. Now they are trying to learn how to fix problems before they have to scrap the product," she says.

Toward that end, the FDA's Center for Drug Evaluation and Research (CDER) began in 2003 putting together guidelines called the Process Analytical Technology (PAT) initiative. The thrust of this non-binding program is to help pharmaceutical and biotech companies figure out how to reduce process variability and build quality into the drug manufacturing process, rather than going through the costly exercise of tacking it on at the end.

PAT can take many different forms, but in general refers to a framework that life sciences manufacturers can use to design, analyze, and control manufacturing processes by measuring quality attributes of raw and in-process materials. Unlike 21 CFR Part 11, it is a voluntary initiative; there are no associated deadlines for compliance.

While other process industries (such as chemicals, oil & gas, and food & beverage) started similar programs years ago, PAT is the first regulatory effort in this sector to focus on process rather than documentation. As such, its potential impact on life sciences is much greater. And while most of the largest pharmaceutical companies in the world have started PAT pilot projects, none is very far along, according to Smith, since the concept is new and requires such sweeping changes in the way these manufacturers do business.

The FDA has taken a strong position with its endorsement of PAT: It's time for pharmas and biotech companies to invest in modern technology. "The whole idea is to bring some science and engineering to the pharma manufacturing process, which has traditionally been very manual," says AMR's Smith. The benefits could be enormous. Chipping away at the scrap rate could add up to huge cost savings for manufacturers. And anything that aims to increase drug quality will benefit consumers by producing safer end products.

To date, the industry has always relied on corrective action. "They manufacture [the product] and then see if there is something out of spec. They find out after the fact and have to make a correction," says Glenn Restivo, marketing manager for Rockwell Automation Inc. (Milwaukee, WI) and the author of a white paper on PAT. "With PAT, the benefit is continuous quality verification. PAT is a paradigm shift. It's not lab-centric."

PAT allows pharma manufacturers to replace some of their incessant testing with detailed explanations of their processes -- a sea change from the way they have done business before. That's one reason companies have been fairly slow to adopt PAT. "We don't today see big projects or corporate deployments," says Frédéric Halley, vice president of marketing for Pertinence, a Paris-based vendor of enterprise manufacturing intelligence software. "Companies are evaluating what they can do. Pharma tends to be conservative and somewhat slow-moving."