Clean-in-Place for the Biopharmaceutical Processes

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The sensitivity of visual inspection is also in debate and known to be specific to the products or compounds themselves. This is another qualitative method that evaluates the test coupons after the cleaning test is completed to see if there is evidence of residues on the surface. The test is only valid for residues of products or compounds that are hydrophobic, so this would exclude the many pharmaceutical products which are hydrophilic.

The test also requires that the water and test materials have been certified to be free of hydrophobic or hydrophilic substances prior to testing. The residues of hydrophilic substances on the surface of the coupons to be tested may result in passing results even though residues are present. Rough or porous surface conditions of the coupon may also interfere with this test. The repeatability of this test method requires that inspectors have been trained in the observation of surfaces for water breaks.

As with the visually clean method, the cleaning test may need to be continued until the water-break free test passes, which can also result in long test times. This quantitative method measures the amount of residue removed from or remaining on the test coupons after the cleaning test. Two or more coupons made of the MOC of the equipment are weighed and then coated with the products being tested and weighed again. These coupons are then subjected to the test conditions and reweighed after the test.

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The difference in weight can tell you either how much product was removed or how much remains see Table 1. There are quantitative methods, such as high-performance liquid chromatography HPLC and total organic carbon TOC analysis, that can measure very low amounts of residue remaining on the test coupons after the cleaning test. The ability of these methods and their sample collection methods usually swab to recover the residues needs to be determined.

Digital image processing and analysis software may also prove helpful in evaluating the removal of residues by analyzing the test surface before and after cleaning. In the gravimetric method, the CEF values are calculated from the weights of coupons before and after cleaning, as shown in Equations 1 and 2.


Note: Either of these ratios can be multiplied by to get the percent remaining or percent removed. Depending on the test conditions, different products will have different CEF values. These CEFs can be used to help answer the seven questions listed above and can also be used to rank process residues based on actual quantitative data. In this first part of the series, we will show how cleanability testing and the CEF can be used to easily answer the first question. Most pharmaceutical facilities manufacture more than one product, and many facilities can manufacture dozens or even more than products.

These facilities will also have dozens, if not more than , pieces of equipment. Regulators have long understood that performing cleaning validation for every product and every piece of equipment would take companies many years to complete. Companies suggested, and regulators agreed, that performing cleaning validation for a worst-case scenario should be sufficient.

Of course, regulators have required a justification for the selection of any worst-case scenario. The question arose as to how to determine which products are the hardest to clean. Since there is no formal definition of hardest-to-clean products, companies have turned to several criteria for selecting them such as solubility, potency, and toxicity of the API, as well as operational experience with the products.

Currently, several regulatory guidances have listed solubility, toxicity, potency, and operational experience some now include cleanability as approaches to determine the hardest-to-clean product. However, a quantitative, scientific method is needed for the determination of hardest-to-clean products. The approaches that have been suggested as indicators of hardest to clean, such as solubility, viscosity, potency, and toxicity, may not be reliable or even justifiable.

Cleanability testing, combined with statistical analysis of cleanability data, can provide such an objective and quantitative alternative to these older approaches. Figure 6 shows a comparison of typical cleanability data CEF remaining for two products. Coupons were prepared in triplicate for both products and tested under the same cleaning conditions.

What is Cleaning in Place (CIP)? | Puritan Products

The two-sample t-Test is a statistical analysis designed to test if there is a difference between two means from two different populations of data. For this cleanability test, there was more residue remaining on the coupons for Product A than for Product C. The graph shows 95 percent confidence intervals for the means of the data, and there is no overlap, indicating the means are different.

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Finally, we can see if the difference between the means is statistically significant by comparing the p-value for the significance level. The typical significance level of 0. The P-Value of 0. In this case, we can clearly see that the cleanability data for the two products are different, the results are statistically significant, and Product A is harder to clean than Product C. Product C.

Figure 7 shows a plot of cleanability data for two products. Again, the CEFs were determined in triplicate for both products. For this cleanability test, the residue levels on the coupons for Product A and Product B appear similar. The 95 percent confidence intervals for the means of the data overlap significantly, indicating the means are probably not different. In this case, we cannot see any difference in the cleanability data between the two products, the results are not statistically different, and we cannot claim Product A is harder to clean than Product B.

Product B.

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ANOVA is a convenient way to compare the means of multiple sets of data, which avoids the problem of increasing the probability of Type I errors using multiple t-Tests if we were to compare the data sets one at a time. In this case, we might believe there is a difference between the cleanability of two products when there is none.

All six were tested using the same conditions time, temperature, etc.

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Product A 3 However, the 95 percent confidence intervals for the means of the data overlap for Products A and B, for Products C and D, and possibly for Products E and F, indicating the means for these pairs may not be different. However, there are additional tests for analyzing ANOVA results that can provide deeper insight into these results.

We can compare the confidence intervals for all possible pairs of the means and show any differences using Tukey's method. But Products E and F are slightly different. Please contact Grantek to request any information you may need for your purified water system, or any pharmaceutical system. We look forward to the opportunity to share our expertise and learn about your needs. A typical system that produces purified water may require three stages: Pretreatment , consisting of filtration to remove particulates and some organics, and de-chlorinization to remove chlorine added by the municipal water treatment system.

Chlorine will degrade stainless steel over time and therefore must be removed from the water before the water enters the pharmaceutical production equipment. Activated carbon filters are typically used to remove chlorine, which can also damage reverse osmosis RO membranes over time. Sediment filters may be used to remove particulates. Final treatment , in which the water is purified via ion exchange, RO, electro deionization, or a combination of methods.

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