Yet, in a survey conducted by Bioprocess Technology Consultants, just 29% of respondents indicated they practiced QbD. Twenty percent of those surveyed believed that QbD is overly time-consuming, 15% felt it would delay regulatory approval, and just 26% felt it reduced time to market.
Furthermore, the 9th Annual Report and Survey of Biopharmaceutical Manufacturing, a 2012 study from BioPlan Associates, reports that PAT ranks at the bottom of currently implemented quality initiatives. The numbers: risk analysis (61%), QbD (43%), lean six sigma (43%), PAT (21%).
The study cites implementation times (71%), insufficient personnel (61%), costs (54%), and regulatory uncertainty (47%) as the principal hurdles to implementing PAT. Respondents citing time and costs were down since BioPlan’s previous survey, which Eric Langer, managing partner at BioPlan and a booster of PAT, attributes to the industry “recognizing the benefits that PAT provides.”
But Langer admits that despite its promise, “implementation of PAT remains slow and uneven, leading some to ask when this initiative will achieve its promise” but is confident that innovation in validated measurement techniques and control systems will eventually win the day. “The success of PAT and QbD will depend on better analytics, which will help biomanufacturers make a strong business case for using these tools to maximize yields, obtain purer product, and minimize quality defects.”
Given the time since FDA’s PAT guidance, and the fact that bioprocess monitoring has been ongoing since the birth of manufacturing biotech, one wonders if biomanufacturers have calculated return-on-investment analyses and simply not liked the numbers.
For example, the batch failure rate for bioprocessing is around 7%. The most common causes for facilities operating above the 1,000 liter scale are contamination (30%), operator error (20%), equipment failure (18%), and failure to meet specifications (13%).
For facilities operating below 1,000 liters, the top three factors are material failure (29%), equipment failure (26%), and product cross-contamination (17%). PAT might avert some failures related to meeting specifications or equipment failure if the specific analytics related to some quality attribute were in place, and if timely remedial action could turn the batch around—big “ifs”. PAT is unlikely to salvage batches that fail due to microbiological contamination or cross-contamination, operator error, or material failure.
Similarly, quality attributes such as protein folding, aggregation, and post-translational modification are difficult to evaluate in real time, particularly in-line or at-line. The possibility of repairing the batch in mid-production assumes processors “understand” the exact cause of the excursion from specifications and can act in time—neither of which is guaranteed.
So questions remain about PAT and QbD—their workability, cost-effectiveness, relationship in the analytic-quality space, and their ability to deliver as promised.
According to Christian Hakemeyer, Ph.D., fermentation manager at Roche Diagnostics, classical analytics still prevail at his company. These include offline biochemical characterization methods for proteins, and online pH and pO2 for cell culture processes. These analytics do not differ from those employed in the manufacture of therapeutics, he says.
That is unlikely to change any time soon, as Roche Diagnostics has no official PAT initiative, “only sporadic technology projects evaluating new analytical methods.”
“So far, the relationship between PAT and QbD remains within the realm of theory,” Dr. Hakenmeyer says. “With the exception of spectroscopic methods for characterizing raw materials, almost no successes are known in bioprocessing.”