Paul Kippax, Product Group Manager, Malvern Instruments, and Chris Aiken, Laboratory Manager, Reading Scientific Services examine the physical sciences involved in developing and validating a laser diffraction particle sizing method for routine application in the laboratory
The Morphologi G3 measures particle size and shape, providing detailed information for formulation development, process troubleshooting and QC
Particle size and particle size distribution are widely recognised as having a significant influence on the behaviour of pharmaceutical ingredients, from the solubility and bioavailability of an active through to the processability and stability of a finished blend. These correlations mean that particle size and particle size distribution are routinely identified as critical quality attributes (CQAs) – characteristics that have a defining influence on clinical efficacy. As a result, pharmaceutical manufacturers often seek to manipulate and control particle size, especially of the active ingredient (API).
ICH guideline Q6A provides a useful decision tree to help determine if a particle size specification is necessary. This guideline suggests that for both solid dosage forms and liquids containing undissolved active ingredients (i.e. drug product suspensions), a specification is required when particle size is critical to any of the following: dissolution, solubility or bioavailability; processability; stability; or product content uniformity.
Laser diffraction is a technique routinely used to meet the resulting need for particle size data, right across the pharmaceutical lifecycle. In a laser diffraction particle size analyser, the particles in a sample are illuminated by a collimated laser beam. The particles scatter the light over a range of angles, with large particles predominantly scattering light with high intensity at narrow angles, and smaller particles producing lower intensity signals over a much wider range of angles. Laser diffraction systems measure the intensity of light scattered by the particles as a function of angle and wavelength. By applying an appropriate light scattering model, such as Mie theory, particle size distribution is calculated directly from the measured scattered light pattern.
Laser diffraction is an ensemble particle sizing technique, which means it provides a complete particle size distribution for all of the particles present within the measurement system at once, rather than building up a distribution from multiple measurements of individual particles. This makes laser diffraction an inherently fast technique. It also has a wide dynamic range, typically measuring particles from 0.1 to 3000µm. This range, which can be extended through the use of certain optical set-ups, comfortably spans the area of interest for most pharmaceutical materials. It is also suitable for both liquid dispersion and dry powder measurement, making it a versatile tool for characterising pharmaceutical powders (including excipients), sprays, aerosols and emulsions.
Developing an analytical method involves the systematic identification and optimisation of the steps needed to take a raw sample through to successful measurement. Here, the principles of Quality by Design (QbD) can be applied, where the requirements for the method are first identified (the Analytical Target Profile) and the CQAs associated with achieving this are assessed.1
Laser diffraction generally involves relatively little sample preparation but presenting the sample in a suitably dispersed state is critical if relevant analysis is to be achieved. The dispersion parameters may therefore be considered as CQAs which must be investigated during method development. Laser diffraction measurements are, as a result, usually preceded by sample dispersion using either liquid or dry techniques.
When dry dispersion is used, the sample is entrained within a high-velocity air stream. Dry dispersion is fast, well-suited to moisture-sensitive materials and accommodates relatively large sample volumes, making sampling easier. Since no dispersant is needed, dry dispersion is environmentally benign and especially suitable when solvent handling is an issue, as in a QC environment, for example. However, the high intensity nature of dry dispersion makes it unsuitable for certain types of samples, most especially those that are friable.
Liquid-based measurements involve mixing the test sample in a suitable dispersant, with water being the preferred choice (dependent on solubility). Surfactants can be used to aid particle wetting, whereas the application of ultrasound or agitation is used to promote dispersion. Dispersion stability can then be achieved by adding stabilisers that reduce the force of adhesion between particles. Verifying complete dispersion and ensuring that the suspension remains stable during analysis are crucial elements of the method development process.
It is vital to confirm that a proposed method behaves robustly before it is put to use
Whether applying wet or dry measurement, it is vital to confirm that a proposed method behaves robustly before it is put to use. A systematic process of testing how the method deals with potential sources of variability in the identified CQAs forms a core part of the validation procedure. Investigation of each CQA allows for the definition of the parameter space (Method Operable Design Region) within which the method meets the Analytical Target Profile and is therefore appropriate for the sample.1 It also quantifies the accuracy of the resulting data, which is essential when it comes to developing specifications to control product performance.
Two concepts are central to the validation process: repeatability and reproducibility. Determining repeatability involves making a minimum of six measurements of the same sample under a defined set of test conditions. This tests the precision of the instrument and the consistency of the sample. Reproducibility is a broader concept that also incorporates sampling. Measuring different samples of the same batch quantifies reproducibility, that is the variability associated with all aspects of the analysis, right through from sampling to measurement. Together, then, reliability and reproducibility figures rate both the inherent performance of the analyser and the suitability of the devised method.
Two concepts are central to the validation process: repeatability and reproducibility
US and European Pharmacopoeias (USP and EP) provide guidance for reproducibility criteria.2,3 However, depending on the use of the data, more stringent acceptance criteria may be advantageous. Validation ensures that the method is fit for purpose, therefore acceptance criteria should be set such that the results can be trusted to drive efficiently the decision-making processes that they have been developed to support.
The following case study provides a practical insight into how the method development process is conducted and the techniques and tools that can be brought to bear to tackle any problems that arise.
A pharmaceutical manufacturer approached RSSL, a company that provides analysis, research, consultancy and training services for the pharma and other industries, for help with a particle sizing method to measure the size of active ingredient particles exiting a crystallisation process. The results from this analysis are used to determine whether the exiting material required milling, or not, ahead of subsequent processing.
The customer had previously developed a liquid dispersion method for this purpose, which had performed well during process development at the lab scale. However, with the manufacturing process progressing into scale-up, the particle sizing technique appeared to be failing. In the lab, almost every batch produced met the particle size specification, but post scale-up the situation was very different, with the number of out-of-specification batches greatly increasing, suggesting far more variability. The particle sizing technique was in the spotlight but there were a number of possible explanations:
The manufacturer came to RSSL for advice and support to:
Approaching this brief systematically, analysis began with an assessment of the feasibility of dry measurement. For dry dispersion measurements, the pressure of the air used for sample dispersion is commonly a CQA for the method. Investigations therefore started with a pressure titration.
Figure 1: A pressure titration for a sample unsuited to dry dispersion
This useful experiment determines whether a sample can be dispersed successfully without causing primary particle damage. It involves measuring particle size as a function of the dispersion pressure with higher dispersion pressures being equated with higher applied dispersion energies. Results exemplifying the pressure titration data measured in this study are shown in Figure 1. As air pressure is increased, the measured particle size falls steadily. There is no plateau on this plot; no range of air pressures at which measured particle size is independent of dispersion pressure. Comparing the measured results with liquid dispersion measurement data (not shown) reveals that at higher pressures the reported particle size data are smaller than observed with liquid dispersion, while at lower pressures the reverse is true.
The above results indicated that dry dispersion is not suitable for this sample. At low air pressures the sample was not dispersed but at higher pressures the particles were essentially milled. There is no measurement area where dispersion can be achieved robustly without primary particle damage. This part of the study therefore confirmed wet dispersion as the better solution for this application.
With dry dispersion ruled out as unsuitable, RSSL initiated a full method development study to determine the best wet dispersion method. The following parameters were identified as CQAs and were therefore investigated systematically:
Dispersant system: A water-based dispersant system was adopted with wetting agents and surfactants incorporated to enhance wettability and improve suspension stability. Conducting an assessment of additives on the basis of their impact on dispersability, refractive index and wettability enabled optimisation of the overall dispersant system.
Dispersion preparation: With the samples exhibiting poor wettability and prone to accumulating static, careful consideration of the practicalities of dispersion was required. Measurements were carried out to assess the benefits of pre-mixing to form a concentrated slurry, and similarly the application of sonication.
Dispersion and measurement time: Because the sample was sparingly soluble in water, dispersion and measurement times were important considerations. A short dispersion time proved inadequate for full sample dispersion, but lengthy contact times reduced the number of fines present, indicating dissolution of the finer portion of the sample. Dispersion and measurement times were optimised within these constraints, a process aided by pre-mixing the sample to a concentrated slurry.
Applying automated imaging to assess dispersion: Throughout the wet development process, automated imaging (Morphologi G3, Malvern Instruments) was used as an orthogonal reference technique to verify the extent of dispersion of the sample and to help move efficiently to an appropriate method. Figure 2 shows example particle images with aggregated material clearly present in one sample but not in the other. By applying appropriate classification criteria, imaging can be used to verify the absence of aggregated material in the sample, and correspondingly the success of dispersion.
Figure 2: Images for aggregated (top frame) and dispersed (bottom frame) samples can be used to verify the success of a dispersion process
With the wet method substantially refined, the focus of the study became validation to ensure the method met the Analytical Target Profile agreed with the customer. The criteria for an appropriate method were based on guidance offered in ICH documentation (Q2, R1)4 and the USP, and the need to demonstrate measurement robustness for both unmilled and milled material. Repeatability and reproducibility tests were conducted for both types of sample. All acceptance criteria were met for the milled samples but unfortunately this was not the case for unmilled samples (here the method failed to perform adequately).
Imaging of the unmilled samples was performed to investigate this problem, which revealed that these contained quite differently shaped particles (see Figure 3). Some consisted of cubic-shaped particles, while others contained far more elongated, needle shaped particles. These results suggested a loss in control of crystal habit and helped to explain the variability in the original wet dispersion measurement technique, although other flaws in the method also became apparent during the study. Correlating the imaging data with the results from the validation exercise showed that the method was robust when cubic shaped particles only were analysed, but not if both types of crystals were included in the validation trial. This was a vital finding, revealed through cross validation with automated imaging.
Figure 3: Variations in crystal shape, with some cubic (top frame) and others more needle-shaped (bottom frame), proved to be a source of variability in the particle sizing method
The technique of laser diffraction calculates particle diameter data on the basis of spherical equivalence. This means that the instrument interprets the detected scattering data to report the particle size distribution of the spheres that would produce similar data. Particle shape deviations away from spherical will therefore have an impact on laser diffraction data. This is why the cubic and needle shaped particles performed differently in the analysis, complicating the validation process.
The above findings mean that unmilled samples can be reported as out of specification either because they are the wrong size or because of the impact of needle-shaped particles on the particle sizing method. Fortunately, the solution in either case was to mill the sample since this reduced size and made the shape of the particles more regular. On the basis of this understanding the customer was therefore able to specify a strategy of milling all unmilled batches that failed to meet the specification. The particle sizing technique was therefore successfully able to provide data to support the decision of whether further milling was required (or not), for all samples – the original intent of the method validation.
Across the pharmaceutical industry the substantial need for particle size and size distribution data is routinely met through the application of laser diffraction analysis. Fast, efficient and widely applicable to the sample types handled by pharmaceutical manufacturers, laser diffraction instrumentation is highly automated and well-suited to daily use within the lab and on plant. However, successful application of the technique relies on robust method development and validation.
This case study provides an example of the steps that may be involved in ensuring a particle sizing method is fit for purpose, and it demonstrates the value of applying automated imaging to troubleshoot analytical issues. Imaging provides visual and statistical data confirming adequate dispersion of a sample and helps to identify features of the particles that may affect analytical results. In this case study, the differentiation of samples on the basis of particle shape proved pivotal in the understanding of variability in the method, and supported the confident development and application of a robust, fit for purpose method.
1. QbD Considerations for Analytical Methods – FDA Perspective, Sharmista Chatterjee, IFPAC Annual Meeting, Baltimore, 25.1. 2013. Accessed online at: http://www.fda.gov/downloads/AboutFDA/CentersOffices/OfficeofMedicalProductsandTobacco/CDER/UCM359266.pdf
2. General Chapter <429>, United States Pharmacopeia, Pharmacopoeial Forum (2005), 31, pp1235–1241
3. General Chapter 2.9.31, European Pharmacopeia.
4. Validation of Analytical Procedures: Text and Methodology (Q2, R1), 2005. Accessed online at: http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Quality/Q2_R1/Step4/Q2_R1__Guideline.pdf