Last month we covered definitions and clarifications in dealing with issues related to cleaning processes and cleaning validation for campaigns. This month we will cover how to select the maximum campaign length. There are two basic considerations for choosing a maximum campaign length. One of the considerations is really related to process validation; that is, is there anything about the length of the campaign that might affect the product quality of the product processed during the campaign. A second consideration is cleaning validation; that is, is there anything in the campaign length that might affect the difficulty of cleaning of the equipment after the final batch of the campaign.
We’ll cover the process validation issue first; if extending a campaign length adversely affects manufactured product quality, then the campaign is too long. How can I tell if product quality is being affected? The obvious answer is to test the products made and determine conformance to specifications. That is, I test product made in batch 1, batch 2 through batch “n”. I look for data and for data trends that either suggests the product is in specification or that it is trending out of specification. What data may I evaluate? Well, it could include any data that I would measure as part of my process validation. However, I should particularly focus on data that may change over time (which may be related to elapsed time or to batch number).
Here are types of data which might suggest limitations on campaign length (those limitations might be on total elapsed time or on total number of batches before Type B cleaning is done).
By product impurity profile, I mean data obtained on product analyses consistent with ICH Q3XXX. For this analysis, I might compare an impurity profile over a number of batches in a campaign. For simplicity in this example, let’s assume I am only looking at “total impurities” as a function of batch number. Here are three examples.
| Batch No. | 1 | 2 | 3 | 4 | 5 |
| Campaign A | 0.021% | 0.019% | 0.020% | 0.021% | 0.020% |
| Campaign B | 0.019% | 0.020% | 0.024% | 0.029% | 0.035% |
| Campaign C | 0.029% | 0.019% | 0.020% | 0.021% | 0.020% |
For Campaign A, there appears to be little change in the impurity profile over time (either as elapsed time or number of batches). For Campaign B there appears to be an increase in the impurities over time. Whether that increase is significant or objectionable has to be decided on an individual basis. In any case, the data suggests that that the trend to increasing impurities may further increase as batches go beyond five. For Campaign C, note that there appears to be a higher impurity level in the first batch of the campaign, with subsequent batches having lower (and more consistent) values. Why might this happen? If it does happen, it is likely that the cause is transfer of residues from the end of campaign cleaning of the previous campaign. That is, if there are residues from the previous product (albeit at an acceptable level from a cleaning validation perspective) left on the cleaned product contact equipment surfaces, those residue are likely to transfer to the first batch of a subsequent campaign, but not the second, third and subsequent batches.
A similar assessment could be made for bioburden or for physical properties. In this evaluation of impurities and/or bioburden, I am not sampling the equipment surfaces between batches in a campaign; rather in most cases it should be acceptable to sample the manufactured product. Any product left on equipment surfaces between batches should have a very similar composition to the last portion of the batch just processed. But, in an abundance of caution, I want data from the beginning, middle and end of each batch; and that data should be available as part of my process validation.
However I get the data, it should provide confidence that during an extended campaign (total elapsed time and total number of batches) the product I am producing is consistently meeting specifications.
Now we get to the second criterion, which is related to the “difficulty of cleaning”. Specifically, does cleaning of a product become more difficult after the nth batch? Realize that it is okay if cleaning becomes more difficult as long as I design my cleaning process with the soil conditions on the equipment surfaces after that nth batch in mind. For clarification, it may be that the difficulty of cleaning does not change with an increase in batch number (this makes life easier); or it may be that if cleaning becomes difficult with increasing number of batches, I just need to make sure that in my cleaning process design I have addressed the situation of that increased difficulty of cleaning.
So, the follow-up question is “How do I determine if cleaning becomes more difficult with successive batches in a campaign?” Well, one obvious answer could be to make the product in a campaign mode and clean it with the same cleaning process after one batch and then after “n” batches, and measure residues as you would in a cleaning qualification protocol. If values are essentially the same, then it is likely that cleaning does not become more difficult with that number of batches and with that elapsed time. It should be obvious, but I must clearly state it, that to demonstrate no change in cleaning difficulty you can’t start a campaign, and within that campaign clean after the first batch, after the fifth batch and then after the tenth batch (if you do that you have not really evaluated the difficulty of cleaning after ten successive batches). You really have to do a one-batch campaign, clean and test; and then do a five batch campaign, clean and test; and then do a ten-batch campaign, clean and test.
In the above example, I discussed showing that values after the different campaign lengths are the same. That is not a firm requirement. The only firm requirement is that the values after the last batch in a campaign are robust enough compared to acceptance criteria. However, what most companies do (or rather what they should do) is to utilize that same robust cleaning procedure for all campaigns regardless of campaign length (as long as the campaign length is within the maximum). Note that such studies may be prospective studies or they may be retrospective studies.
It may also be possible to design lab studies to simulate the cleaning of the equipment as it might be after manufacture of the last batch of a campaign. This may involve the amount of product on the surface, whether the product has undergone physical changes (such as being “compacted”), and whether the product has dried on the surface. You will have to use your judgment here to design a lab study that might be predictive of what happens on full scale equipment during normal production. Another option is to select a current cleaning process on a product which is expected to have an equivalent difficulty of cleaning as compared to the new product. In any case, it is important that acceptable performance of the cleaning process be confirmed under actual manufacturing conditions.
Last month we covered basic definitions, and this month we covered how to select what might be a maximum campaign length. Next month we will cover how to validate for that maximum campaign length.