The investigation of out of specification (OOS) results is a regulatory requirement in a GMP laboratory and these investigations are intensively scrutinised by health authority inspectors. The purpose of this article is to provide 5 ‘Golden Rules’ which will make sure that investigations are both effective and inspection ready. Rather than use the term OOS in the article, the term out of expectation (OOE) will be used throughout and should be interpreted as a catch all term which includes all OOS, out of trend (OOT) and atypical results since all of these should be investigated in a similar way.
OOE investigations are crucial in analytical labs for two reasons. The first is fairly obvious; if a reportable result is not as expected then it is important to find out whether it is a true result or if the lab has made a mistake. The second reason doesn’t seem to be appreciated by some labs, namely that how a lab approaches the investigation of OOE results provides an indicator of how effective they are. High numbers of lab errors may indicate underlying problems, and the quality of the investigation indicates their scientific know-how and commitment to continuous improvement.
Rule #1: Do it because you want to, not because you have to
An investigation into an OOE result is required by cGMP but it is also a great opportunity for improvement in the lab. Investigations that are driven by too much compliance and not enough science are easy to spot and seldom stand up to scrutiny. Typical signs include:
- A formulaic approach without due consideration of the available evidence where the root cause appears to be pre-selected rather than identified as a result of the investigation. (see Rule #2)
- Use of imprecise language leading to claims that hypotheses are proven when in fact they are just shown to be probable. (See Rule #3)
- The investigation has been closed too soon and the true root cause(s) have not been identified. (See Rule #4)
- Inadequate corrective actions and preventative actions (CAPAs) are generated which don’t address the problem(s) and root cause(s) of the OOE result, which then leads to repeat occurrences of the same issues. (See Rule #5)
Ultimately, an investigation is required anyway so it makes good sense to make the most of the opportunity for improvement that it presents.
Rule #2: Always follow the evidence
Poor OOE investigations will not fully consider the available evidence. Often this is because the investigator is not suitably qualified and experienced to lead the investigation. They may jump to conclusions that are not supported by the evidence, or they may investigate irrelevant hypotheses.
A good checklist can be extremely helpful to assist in gathering evidence relating to an OOE, and is a very popular approach, but it should act as a useful tool, not a crutch. Unfortunately checklists can become something of a tick-box exercise, and combined with an SOP which contains ‘examples’ of hypotheses, may lead to the same formulaic approach being used for the hypothesis testing in all investigations. Scientific rigour is essential in OOE investigations and the people involved need to be competent and capable for the task.
Rule #3: Say what you mean, precisely
The language used in an OOE investigation report needs to be precise. There is a big difference between a lab error that is ‘proven’ and one that is ‘probable’. The terms chosen need to reflect the strength of the available evidence. Words like ‘eliminate’, ‘proven’, and ‘demonstrated’ should be used only if the evidence is definitive. When evidence is present but indefinite, then words such as ‘suggests’, ‘likely’ and ‘probable’ are more appropriate. Well written reports, which include precise use of words, will provide a scientific record of the investigation that is clear and understandable, even years after the event.
Rule #4: It’s not over till it’s over
Once the reason for the OOS result has been found, the temptation is to close out the investigation as quickly as possible, particularly when a lab error has been identified and the batch being tested now needs to be released. However, the reason for the OOE result is not the same as the underlying root cause and often there is more than one root cause which needs to be fully investigated if reoccurrence is to be prevented.
An example could be where an inaccurate assay value was found to be due to a problem with a reference standard. Although the OOE investigation has correctly determined that the reference standard caused the OOE result, the root cause has not been identified. Further investigation is needed to find out why the reference standard became compromised, whether it affected just this standard or are all the reference standards now suspect, and whether the reference standard management system is fit for purpose.
Rule #5: Don’t let it happen again
The CAPAs that come out of an OOE investigation should meaningfully address the underlying root causes of the OOE result so that appropriate corrective and preventative actions may be implemented. Retraining the analyst is rarely enough to achieve this but it is probably one of the most common CAPAs in the case of a lab error being identified. Rather than telling the analyst to ‘get it right’ the next time, it would make more sense to look at the initial training that was delivered and discover whether it was adequate, and also whether the mistake may happen to other analysts that underwent the same training.
Repeat instances of the same problem are a clear indication that a lab has not embraced an opportunity for improvement. Good labs will trend CAPAs and assess them for effectiveness.
Following these five rules will ensure that OOE investigations are conducted in a scientific and meaningful manner, with each instance providing a genuine opportunity for improvement. Hypothesis testing will relate to the available evidence and will be interpreted with clarity, the true underlying root causes will be identified, and appropriate CAPAs put in place to correct the problem and prevent reoccurrence.
(Note: This article was originally posted on Pharmaceutical Online)
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