Future Trends in Analytical Method Validation

11 Future Trends in Analytical Method Validation

David Rudd

11.1 Introduction

Analytical method validation has evolved successfully over a number of years into the formalised, well-founded process which we recognise today. The key elements of method performance (accuracy, precision, sensitivity, etc.) are well-established, at least for laboratory-based pharmaceutical analysis, and are universally recognised as

demonstrated. For routine application of methods in a quality control or stability testing environment, for example, it is questionable whether there is any need for method validation concepts to evolve from this current position. While advances continue to be made in measurement technology within existing, well-established pharmaceutical analytical techniques (high pressure liquid chromatography or UV-visible spectrophotometry, for example), the basic requirements of method vali- dation for such techniques remain largely unchanged.

However, there is, at present, a substantial shift occurring in the way in which pharmaceutical products and manufacturing processes are being developed – with a consequent impact on the type of analytical methodology required to support this new approach. Emerging regulatory [1] and business factors [2] are placing much

ceutical development and manufacture, such that the traditional approach of dem- onstrating and sanctioning product quality using laboratory-based finished product testing is no longer considered wholly appropriate.

within the current approach to pharmaceutical development and manufacture, namely that finished product testing can only reveal problems relating to product quality, but can do little to rectify or prevent such problems occurring. For example, testing a batch of tablets in accordance with a pharmacopoeial specification for Uni- formity of Content of individual dosage units will reveal that, perhaps, the powder blending process prior to compression of the tablets may have failed to achieve chemical homogeneity but, although the batch may be prevented from reaching the unsuspecting public, it remains unsatisfactory in terms of quality and safety and will need to be discarded or destroyed. More significantly, unless additional informa-

Method Validation in Pharmaceutical Analysis. A Guide to Best Practice. Joachim Ermer, John H. McB. Miller (Eds.) ISBN: 3-527-31255-2

11 Future Trends in Analytical Method Validation

tion is gathered regarding the cause of the powder blending issue, there is every likelihood that subsequent batches will suffer from the same problems. Finished

tunate faller, but doing little to prevent the fall in the first place. product will possess the appropriate, pre-defined level of quality based on the appli-

cation of a robust, scientifically well-understood manufacturing process coupled with assurance of the quality and suitability of the input raw materials.

This is achieved by appropriate characterisation of raw materials, not just from the traditional chemical and physical standpoint (for example chemical purity, water content, particle size, etc.), but also by establishing the suitability for subsequent pro-

ing characteristics for materials which need to be blended with one another, or to con- firm the flow properties where material transfer during processing is unavoidable.

or processes will need to be able to accommodate the inevitable variations in raw material quality and/or characteristics. Even with stringent controls based on chemi- cal and physical specifications, as well as aspects of processability, raw materials con- tinue to exhibit some variability from batch to batch, or from supplier to supplier, in terms of quality. As long as a specification parameter contains allowable ranges (for example, chemical purity might be specified as a range from 98 to 102% by weight), variation in quality remains inevitable.

Thus, manufacturing processes must be able to deal with these typical variations in such a way that the quality of their output remains consistent. This means that the manufacturing process itself must possess a degree of controlled flexibility – for if a truly fixed process is applied to variable input (i.e. variable raw materials), inevi- tably only variable output (finished product) will be achieved.

Such flexibility can only be established during the development of the manufac- turing process, such that a full understanding of the complex inter-relationship be- tween process operating parameters and the quality attributes of the finished prod-

have analytical measurement capability which allows information to be obtained within a very short time frame (relative to the process) such that the influence of changes in process operating parameters can be readily seen in terms of their impact on the finished product quality attributes.

surement or set of measurements which is made during the operation of the process itself (probably within the production environment), rather than after completion of the process and on the output of the process (and probably within a laboratory envi-

the process understanding, and subsequently the process control, needed in order to

guidance [3, 4] remain applicable, there are nevertheless a number of further consid- guidance [3, 4] remain applicable, there are nevertheless a number of further consid-

One of the first realisations, when recognising the need to introduce analytical assess- ment earlier into the pharmaceutical manufacturing process, is that it is seldom pos- sible, or indeed appropriate, simply to transpose the laboratory-based measurement technique into the production environment. For example, although high pressure liq- uid chromatography (HPLC) is used extensively in the laboratory to confirm powder blend homogeneity during pharmaceutical blending processes, the equipment remains unsuitable for routine operation in the potentially dusty manufacturing environment. In addition, the difficulty in taking randomised, representative sam- ples (which themselves may require fairly complex dissolution, dilution and/or filtra- tion prior to the HPLC measurement step) is likely to limit the effectiveness of the

In order to overcome these shortcomings, alternative analytical methodology is

be incorporated. Near infra-red spectroscopy (NIR), with its information-rich data output, minimal sample pre-treatment and capability for rapid measurement, is particularly suitable, for example, for powder blend homogeneity testing [5]. In turn, the use of passive acoustics for the endpoint control of high shear tablet granulation processes [6], especially in respect of the physical attributes of the granule, is also well-established.

Both of these technologies share common features characteristic of successful

a) Non-invasive

This means that no discrete sampling is required. Indeed, such technologies

ple’. Spectroscopic techniques may achieve this using fibre optic probes, for example, or simply by introducing electromagnetic radiation into the system via external windows on the process equipment. Similarly, acoustic tech- niques depend on the use of transducers fixed to the outside of the process

tual’ samples (the size of which are calculable based on the optical or acoustic configuration of the equipment, frequency of measurement, depth of pene- tration, etc.), the fact remains that no discrete sample needs to be removed from the system under investigation.

b) Continuous output

Unlike laboratory-based methodology which generally gives a single result for each available sample (an HPLC assay on a batch of tablets, for example),

11 Future Trends in Analytical Method Validation

NIR spectra can be generated and displayed several times each second, while acoustic signals are usually averages of measurements made typically at rates up to 2000 times per second.

c) Information-rich to contain information about a number of quality attributes of the system

under investigation. For example, NIR spectroscopic data includes informa- tion about chemical composition (and, hence, can be used for assay determi- nation), but is also affected by some physical aspects of the sample (and may, therefore, be used to assess properties such as particle size, granularity etc). As a result, simple uni-variate treatment of the data generated may be inap-

approach is likely to be required.

d) Application to dynamic systems system which is being measured is itself changing during the measurement

process. Unless a manufacturing processes is deliberately halted during the measurement step (an action which, if absolutely critical in obtaining mean- ingful analytical data, would bring into question the suitability of the

sufficiently high that meaningful averages can be taken. This will help to overcome the transient effect of measurement parameters changing due to the dynamics of the system under investigation.

Based on the characteristics described above, there are a number of consequences when the issue of method validation is addressed.

ment actually relate to? For a dynamic system, is it fair to say that the true sample is the entire batch or bulk material contained within the processing equipment? Clearly the answer is only in the affirmative when a sufficiently large number of rep-

Thus, a key aspect of the validation of any non-invasive analytical method must involve an understanding of the required measurement frequency and duration (i.e. the overall number of replicate measurements) as well as the calculation (or, at least,

This latter point is particularly true when attempting to establish chemical homo- geneity in a bulk system, for example, as statistical criteria for acceptance are inevi- tably based on the variation seen in individual dosage units of defined size and/or mass. At the regulatory level, there is clear published guidance [7] regarding the lev- This latter point is particularly true when attempting to establish chemical homo- geneity in a bulk system, for example, as statistical criteria for acceptance are inevi- tably based on the variation seen in individual dosage units of defined size and/or mass. At the regulatory level, there is clear published guidance [7] regarding the lev-

sample size has been achieved. Such considerations will also allow the data process- ing algorithms regarding signal averaging to be established – in turn reflecting the likely rate of change which the measurement technique experiences due to the dynamics of the system under investigation. All in all, this is a critical aspect in terms of demonstrating method suitability, depending as it clearly does on a number of dynamic factors.

Figure 11-1 Variation in replicate NIR spectra (n = 12) versus time of blending.

As an example of a typical application of the approach described, Figure 11-1 rep- sample size obtained with the particular optical configuration used, the endpoint of

the blending process was established by calculating the variation in successive sets of twelve NIR spectra. Part of the validation for this method involves the justification for basing the conclusions on sets of twelve spectra.

ment device and the system under investigation. Even when a fibre optic probe is introduced into a pharmaceutical manufacturing process, but especially when sen- sors or detectors are placed on the outside of process container walls, questions arise concerning the reproducibility of location of these sensors or probes.

Are the sensors positioned correctly and are they positioned in exactly the same locations as before? If not, the quality of the data obtained will inevitably be reduced and, in the worst case, may not truly relate to the system under investigation. Also, if the sensors need to be mechanically fixed to the outside of the process container, how critical and how reproducible is the fixing process? The quality of an acoustic signal is heavily dependent on the acoustic coupling between the sensor and the pro- cess container and needs to be consistently achieved if data are to be compared from one set of measurements to another.

Finally, in considering the validation aspects of non-invasive, sensor-based meth- odology, the question of the number of sensors must also be addressed. It would be wrong to assume that one sensor alone will always be sufficient to monitor and con-

11 Future Trends in Analytical Method Validation

trol a given pharmaceutical process. On the other hand, multiple sensors may also

be unnecessary, providing redundant or, in the worst case, conflicting information. It becomes necessary, therefore, to establish the optimal number of sensors during

of the chosen number during subsequent usage and routine application. regulatory level. With measurements being taken at an extremely high frequency

(especially compared to those obtained during conventional, laboratory-based test- ing) and over a relatively long period of time (i.e. the duration of the manufacturing process), the probability of obtaining a high number of spurious or atypical results is increased (see Chapter 10). Although such results will be heavily outweighed by

must clearly be addressed. Part of the validation process must, therefore, incorporate a thorough review of

meaningful specification and acceptance criteria may also be developed to reflect the suitability of the method for control and routine application. It would be naive to assume that atypical data will not be generated during routine application – espe-

gained during its development, will allow the issue of atypical data to be addressed. contain information relating simultaneously to a number of quality attributes. The

processing of such data sets, therefore, requires the use of multi-variate statistical techniques in order to understand the relationship between the analytical measure- ments and the quality attributes of the system under investigation.

Generally, although well-established statistical methods may be used, the valida- ate multi-variate techniques are being employed. Guidance is available for NIR

methodology in particular [8], showing how multi-variate calibration models can be developed successfully but, in general, an empirical, but scientifically justifiable approach may need to be taken for less mature technologies (for example, acoustics where, for pharmaceutical applications, the complexity of the signals obtained cur- rently preclude trivial description). Indeed, for acoustic methodology, in particular, there may be more value in using data treatment techniques which reveal distinctive features of the signals obtained rather than trying to condense the relevant process information into oversimplified single numbers or quantities.

11.4 Additional Validation Factors 11.4

Additional Validation Factors 11.4.1

To Calibrate or not to Calibrate?

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