PAT in Perspective: Drugs Are Not Potato Chips

Although PAT and technologies like NIR are important, there still are things that they cannot do, concedes NIR expert Emil Ciurczak. Are advocates within the industry suspending rational and much needed skepticism, he asks, when they talk about eliminating final product stability testing altogether?

By Emil W. Ciurczak, Cadrai Technology Group

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Emil Ciurczak

Ciurczak

The U.S. Food and Drug Agency (FDA) began a renaissance in process analysis with its process analytical technology (PAT) initiative and with it made chemometrics a “rock star.” The goal of the FDA’s PAT Guidance is to understand and control the disparate steps in the manufacture of solid dosage forms.

This is an ambitious endpoint and should not be thought of as simple or quick to achieve. PAT’s goal is to help pharmaceutical companies achieve the same level of understanding and control over the final product already enjoyed by the chemical, petroleum, polymer and food industries.

The only caveat is that life-saving drugs are not potato chips. Predicting the salt level of chips is not on par with predicting the blood level of a drug. Saying that a process resembles previous processes does not guarantee product equivalence without some other safeguards in place.

Why NIR is a Major Actor in PAT

I will attempt to summarize the reasons succinctly. NIR, by its nature and history of application, became the first analytical technique to require computer algorithms. Used in the food and agriculture industries for years, it was later “discovered” by other industries: chemical, polymer, paper, textile and, yes, even pharmaceutical.

Normally, NIR is applied to mixtures of materials (including natural products), usually in the diffuse reflection mode. The signal generated contains both chemical and physical information; the physical usually dominates the spectrum. Because of this, higher math is needed to extract the chemical information. Principal Component Analysis (PCA) and Partial Least Squares (PLS) are two commonly used algorithms.

What should occur, if chemometric principles are applied correctly, is that through appropriate design of experiments (DoE) and selection rules, process samples are used to build NIR equations. The analyst must use samples with as many different attributes and from as many manufacturing conditions as one expects to find in manufacturing and clinical settings. Because we cannot envision every possible variation in the process (differences in particle size, new raw material sources, levels of hydration, new/ rebuilt process equipment, polymorphic changes, blending distributions, etc.) or every possible clinical ramification of the designed dosage form, we need to build in a change mechanism — the control space — when we construct the equation.

To avoid having to go through the currently required “change control” process every time a process hiccup or suspect sample arises, manufacturers can address changes as they occur using process control strategies. Thus, when the equation gives a suspicious prediction, we have a mechanism for dealing effectively with the change in “real time.” That is, we design the process and the algorithm to anticipate gaps and to correct predictions in the face of potential process changes.

Despite having to defend the term “predict” to management, that is what Near Infrared does: predict, not assay. The legal definition of “assay” is simply that, in an assay, a method compares an analyte directly with a standard material. For instance, HPLC might use a USP reference to compare with the substance in a tablet. We inject known concentrations of the reference sample into the HPLC before and after unknown samples. The identity and concentration of the unknown are calculated from a simple Beer’s law plot at a set wavelength. Titrations, both for assay and Karl Fisher for water, polarography, gas chromatography, and any other assay technique that uses a reference sample is, by definition, an “assay.”

When a standard reference tablet, capsule, or an intermediate material is not available (essentially in every pharmaceutical process), an equation is built from historical samples. The instrument itself becomes the reference. We measure the wavelength accuracy, light level, noise, linearity and a few other parameters to ensure the instrument is performing to manufacturers’ specifications. We call the analysis, based on previous samples and a well-tuned instrument, a “prediction.”

One of these things is not like the other: Drugs are not potato chips, stresses Ciurczak. Predicting the salt level of chips is not on par with predicting the blood level of a drug. Saying that a process resembles previous processes does not guarantee product equivalence without some other safeguards in place.

In reality, it is not a prediction, but a correlation. It uses statistics to show, based on historical data, that the probability of a characteristic being within limits is satisfactory. It involves “risk assessment,” if you will. A statistical correlation is not a prediction, hence the need for appropriate design and risk management tools.

PAT

The simple reason that process tests such as NIR are the backbone of PAT is speed and ease of use. Most assays are both slow and destructive. Both attributes are drawbacks for large samples and a fast process. What PAT seeks is large numbers of rapid results that indicate where the process is and where it is going.

Chemometric analysis of monitoring instruments provides this control. Methods such as NIR, FT-IR, thermal effusivity, acoustics, Raman, rapid LC, pH, temperature, flow rates, etc. give a signal used in following the process. Based on previous lots and their associated historical data, adjustments bring the batch into “compliance.” Making sure each step gives the same “signals” is one way to assure that each lot of material will produce the same product each time.

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