The validation of
analytical methods is undoubtedly a difficult and complex task. Unfortunately
this means that mistakes are all too common. As a trainer and consultant in
this area I thought it might be useful to take a look at some common mistakes
and how to avoid them. In this series of articles I will pick out some examples
for discussion related to the method performance characteristics as listed in
the current ICH guidance, ICH Q2(R1), namely: Specificity; Robustness;
Accuracy; Precision; Linearity; Range; Quantitation limit; and Detection limit.
In previous articles I wrote about some common mistakes
associated with ‘Specificity’ and 'Robustness'. This time I’ll take a look at
‘Accuracy’. The common mistakes that I have selected for discussion are:
1.
Not
evaluating accuracy in the presence of the sample matrix components
2.
Performing
replicate measurements instead of replicate preparations
3.
Setting
inappropriate acceptance criteria
The definition of accuracy given in the ICH guideline is as
follows: ‘The accuracy of an analytical procedure expresses the closeness of
agreement between the value which is accepted either as a conventional true
value or an accepted reference value and the value found.’ This closeness of
agreement is determined in accuracy experiments and expressed as a difference,
referred to as the bias of the method. The acceptance criterion for accuracy
defines how big you are going to let the bias be and still consider the method
suitable for its intended purpose.
The term accuracy has also been defined by ISO to be a
combination of systematic errors (bias) and random errors (precision) and there
is a note about this in the USP method validation chapter, <1225>: ‘A
note on terminology: The definition of accuracy in 〈1225〉
and ICH Q2 corresponds to unbiasedness only. In the International vocabulary of
Metrology (VIM) and documents of the International Organization for
Standardization (ISO), “accuracy”
has a different meaning. In ISO, accuracy combines the concepts of unbiasedness
(termed “trueness”) and precision.’
From the point of view of performing validation, the
difference in the definitions doesn’t make a lot of difference, we usually
calculate both bias and precision from the experimental data generated in
accuracy experiments. Personally I prefer the ISO definition of accuracy.
Mistake 1: Not
evaluating accuracy in the presence of the sample matrix components
Since the purpose of the accuracy experiments is to evaluate
the bias of the method, the experiments that are performed need to include all
the potential sources of that bias. This means that the samples which are
prepared should be as close as possible to the real thing. If the sample matrix
prepared for the accuracy experiments is not representative of the real sample
matrix then a source of bias can easily be missed or underestimated.
TIP: The samples created
for accuracy experiments should be made to be as close as possible to the
samples which will be tested by the method. Ideally these ‘pseudo-samples’ will
be identical to real samples except that the amount of the component of
interest (the true value) is known. This can be very difficult for some types
of sample matrix, particularly solids where the component of interest is
present at low amounts (e.g., impurities determination).
For impurities analysis, it may be necessary to prepare the
accuracy samples by using spiking solutions to introduce known amounts of
material into the sample matrix. Although this carries the risk of ignoring the
potential bias resulting from the extraction of the impurity present as a solid
into a solution, there isn’t really a workable alternative.
Mistake 2: Performing
replicate measurements instead of replicate preparations
Performing replicate preparations of accuracy
‘pseudo-samples’ allows a better evaluation of what differences in the data are
due to the bias and what are due to variability of the method, the precision. A
minimum of 9 replicates are advised by the ICH guidance and these should be
separate preparations. For solids, this could be 9 separate weighings into 9
separate volumetric flasks, as per the method.
However, the preparation does depend on the nature of the
sample matrix and the practicality of controlling the known value for the
component of interest. As discussed above, sometimes in the case of impurities
methods, solutions may be required for practical reasons even though the sample
matrix exists as a solid. In this case 9 separate weighings does not offer more
representative ‘pseudo-samples’ and thus a single stock solution for the
impurity would probably be a better choice.
TIP: Assess the sample
matrix and try to prepare separate replicates when possible so that the data
produced is as representative as possible and includes typical sources of
variability.
Mistake 3: Setting
inappropriate acceptance criteria
As mentioned previously, the acceptance criterion for
accuracy is based on how much bias you will allow in the results from the
method. It is obviously better not to have any bias in a method but there is
always a certain amount of potential bias associated with the combination of
the sample matrix, the level of the components of interest in the sample, and
the instrumentation used for the measurement. For the method to be capable the
bias needs to be less than the specification for the result. For example, if a
drug substance specification requires that there must be between 99 to 101 %w/w
of the drug present, then a method which has a bias of 2% is not going to be
acceptable.
TIP: Make sure that the
acceptance criteria set for accuracy in method validation are compatible with
the requirements for the method, and in particular, the specification for the
test.
References
In the next instalment, I will write about common validation
mistakes for the method performance characteristic of precision. If you would
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If you would like to learn more about method validation, and
method transfer, then you may be interested in the 3 day course on the topic
from Mourne Training Services Ltd. The course has two versions, one applied to
small, traditional pharmaceutical molecules and one for large,
biological/biotechnology derived molecules. Visit the MTS website for more
information.
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