Winning the Space Race

Complying with ICH Q8 requires knowledge of three key spaces: design, technology and regulatory. Here’s how one company used Design of Experiments to troubleshoot variability in the validation of a controlled-release pain reliever.

By Jason Kamm, Tunnell Consulting, Inc.

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ICH (the International Conference on Harmonization, www.ich.org) Q8 and the Quality by Design (QbD) guidance issued by the FDA offer pharmaceutical manufacturers a significant opportunity to design quality into their manufacturing processes instead of inspecting it in after the fact. Companies that take advantage of Q8 report seeing fewer deviations and rejected batches, reducing risk and easing regulatory compliance burdens, while achieving continuous improvement. The key to achieving QbD is to successively map and master three “spaces”:

  • Technologic (or knowledge) space –
      the multi-dimensional combination and interactions of material attributes and process parameters that affect product quality.


  • Design space –
      a subset of the technologic space, consisting of the multi-dimensional combination and interaction of input variables and process parameters that have been demonstrated to provide assurance of quality.[1]


  • Regulatory space –
    or what a company can do and expect – within the regulatory framework based on the design space. When a manufacturer understands the design space, the manufacturing processes within that design space can be continuously improved without further regulatory review. The manufacturer gains more regulatory room in which to operate and the FDA can be more flexible in its approach, using, for example, risk-based approaches to reviews and inspections.

Using the example of a major manufacturer’s experience with validation – one of the key steps in drug development – this article will demonstrate how pharmaceutical manufacturers can use Design of Experiments (DoE) and other proven statistical and scientific techniques to uncover the design space and reap operational and business benefits.

From One-Factor to Multiple-Factor Analysis

To understand the power and the promise of these methods, consider the case of a new solid-dose, 24-hour controlled-release product for pain management. FDA had approved the product, but it had not yet been validated. As you know, once validation begins, process parameters are historically difficult to change. It was critical to maximize process understanding before embarking on validation, since failure to satisfy regulators with regard to validation would lead to costly delays and rejections.

Unfortunately, there was a problem with the product. It had encountered wide variations in its dissolution rate – the speed with which the drug gets into solution form in the body. These variations presented issues both of safety and of efficacy. Dissolution sometimes occurred too rapidly, which could be lethal in some patients, and sometimes too slowly, which could cause patients to suffer unnecessarily.

When inefficient investigative methods are used to troubleshoot, it can take a great deal of time to understand the root causes of problems such as this. In some cases, such methods may fail altogether, so the company wanted to ensure that it was using the best approach.

It was first necessary to understand the basic process. The product is produced via hot-melt extrusion. Raw material in powdered form is deposited in one end of the extruder and then pushed by mixing screws through the pipeline and melted as it passes over electrically heated thermal blocks to the other end, where it is extruded in strands of polymer. The polymer strands are then pelletized (chopped into pellets) and put into capsules.

Although there were numerous theories about the source of the problem, the manufacturer did not know whether the dissolution problems were related to the active pharmaceutical ingredient (API), the excipient, variables in the manufacturing process, or some combination of all these factors. In the absence of any clear culprit, the manufacturer created a number of small-scale batches from different lots of raw material and manufactured several batches using various process parameters in hopes of replicating the problem. Unfortunately, this one-factor-at-a-time approach did not lead to a repetition of the dissolution problem.

Locking Down Process Parameters: Short-Sighted

Moreover, even if this approach had resulted in the adjustment of a process parameter to yield in-spec product, the resulting improvement would likely have been short-lived. Historically, many manufacturers have taken this approach, and locked down process parameters.

The problem is that differing batches of raw materials are never entirely uniform over the long term and eventually, bad batches recur. Establishing flexible process parameters would allow for much more effective problem-solving and get to the heart of the problem once and for all. Fortunately, it is just this kind of approach that is envisioned in ICH Q8 and advanced by the FDA’s Process Analytical Technologies (PAT) initiative, whose goal is to improve the control and understanding of drug manufacturing processes.

Understanding Technologic Space

Frustrated with the results of one-factor analysis and seeing an opportunity to take advantage of ICH Q8, the manufacturer formed a cross-functional team including members from formulation development and analytical research, as well as process consultants, to undertake further investigation. To develop some initial hypotheses, the team reviewed all of the available historical data about the production of the product. They also interviewed a large cross section of the company’s staff, including personnel from R&D, analytical development, and management as well as operators.

Although they couldn’t make an immediate conclusion, this qualitative and quantitative approach enabled the team to narrow down the range of possible causes to nine potential variables: four properties of the raw material (three related to the API and one related to an excipient property) and five process variables such as temperature, feed rate, and screw speed. From this technologic space – the possible combinations of variables most likely to affect the dissolution rate for better or for worse – the team then began to carve out an understanding of the design space: the multi-dimensional universe of process parameters and input variables within which a satisfactory dissolution rate could be assured.

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