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Pharmaceutical Manufacturing and Packing Sourcer
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Achieving an acceptable level of blend uniformity (BU) during
pharmaceutical manufacture is a necessary prerequisite for delivering
content uniformity; inhomogeneity and a skewed active pharmaceutical
ingredient (API) to excipient ratio could affect drug bioavailability
and efficacy, potentially inducing an adverse response in patients.
However,
achieving BU can be difficult, especially when the ratio between the
API(s) and excipients is high. For example, a cough and cold remedy may
contain 5mg of phenylephrine in a 3,200mg effervescent tablet – a ratio
of 1:640. Ensuring an equal distribution of API molecules under such
conditions often requires specific blending processes and ongoing
process monitoring to ensure BU remains at the desired level.
Commonly,
all ingredients are blended in free-fall or high-shear mixers with
pre-set rotation speeds and blending times. Blending parameters are
often based more on experience than on analytical data. With Quality by
Design (QbD) now being frequently required by regulatory bodies to
ensure that pharmaceutical products are of the quality necessary for
patient use, blending specifications would ideally be based on a
data-driven approach (1).
Fortunately, as process analytical
technology (PAT) tools optimised for this purpose become more readily
available, these requirements can be fulfilled in an easier and simpler
manner than with many traditional off-line approaches (2-4).
Implementation of PAT means that qualities pertaining to both
manufacturing equipment (critical process parameters) and the product
(critical quality attributes) are carefully controlled and defined based
on scientific data. This allows manufacturers to generate products of a
consistent quality, contributing to a potential reduction in both
out-of- specification batches and overall costs. PAT tools – such as
multivariate data analysis (MVDA), modern spectroscopic instruments (for
realtime measurements) and continuous improvement – allow for faster,
more informed decision-making during the manufacturing process.
Quality by Design
QbD
represents a paradigm shift away from an experience-based approach to a
systematic, risk-based one. To underline the importance of this method,
a 2007 report from the FDA emphasised that: “Quality should be built
into a product with a thorough understanding of the product and process
by which it is developed and manufactured, along with a knowledge of the
risks involved in manufacturing the product and how best to mitigate
those risks” (5).
Furthermore, the FDA’s QbD approach was supported by guidance from the ICH; notably, the implementation of directives ICH Q8: Pharmaceutical Development, ICH Q9: Quality Risk Management, and ICH Q10: Pharmaceutical Quality System (6).
As
of January 2013, the FDA also requires generic drug manufacturers to
implement QbD into their abbreviated new drug applications (ANDA) (7).
The FDA and EMA have recently extended a pilot programme of QbD parallel
assessment in order to ensure consistent adherence to international
guidelines related to QbD – an approach that will promote the
consistency of product quality throughout the EU and US (8). Given these
directives, it is likely that the industry will soon be employing and
insisting upon a QbD approach throughout pharma manufacturing as
standard.
Testing Technology
Many of the
technologies facilitating the blending process have advanced
significantly since their inception. Techniques such as near-infrared
(NIR) and Raman spectroscopy are increasingly common and are being
paired with chemometric techniques and MVDA to improve the accuracy of
data capture and evaluation. These spectroscopic techniques allow for
on- and in-line measurements coupled with active process controls
(closedloop control), permitting a very fast and less invasive data
acquisition process compared with traditional analytical methods, as well as being consistent with the FDA’s PAT initiative (4).
Blending Procedure
While
there are a variety of blending techniques available, two commonly
employed approaches involve the use of free-fall and high-shear mixers.
Setting
the correct blending time based on empirical investigations is also
critical: a blending time that is too short can lead to inhomogeneity,
whereas blending times set too long can result in downstream processing
issues (such as hampering flowability due to particle size). Incorrect
blending times can adversely affect not only the properties of the final
product, but also costs.
The current practice of determining
the optimal blending parameters to achieve a uniform blend in these
devices consists principally of blending for a predetermined length of
time, stopping the device and manually removing a specimen. This
specimen is then analysed by traditional methods such as
ultraviolet-spectroscopy or high-performance liquid chromatography
(HPLC) (9), which are often time-consuming and invasive, while also
being a potential source of contamination. Fortunately, NIR spectroscopy
is well-positioned to overcome many of these drawbacks.
Qualitative and Quantitative
NIR spectroscopy is an innovative approach to monitoring and controlling
BU. It offers a number of benefi ts, primarily because the analytical
technology is in-line and provides spectral data at an instant, avoiding
the need to interrupt the process. A mobile NIR spectrometer is
directly attached to the blender and linked to a computer. Data is
recorded through a sapphire window in the blending container, while a
three-dimensional position sensor and software-controlled trigger-switch
initiates the capture of spectral data (approximately 10 spectra per
rotation). Data are only captured when the product mixture is present at
the window. Spectral data are then sent via a wireless local area
network to a computer for real-time analysis.
The most common
method of assessing BU from these data is by analysing the standard
deviation of the spectra, known as a moving block standard deviation
test. The BU end-point has been reached once the variation in the
spectra readout is consistently below a certain critical limit for a
specified period of time. NIR models constructed using this data can be
either qualitative or quantitative.
Content Uniformity
From
both a quality and regulatory stance, QbD continues to grow in
importance and offers substantial potential savings to process costs. As
blending is frequently an integral part of creating a high-quality
pharmaceutical product, it makes sense to base blending parameters upon
evidence rather than experience.
The ever-evolving
sophistication of PAT tools and processes specifically developed for
this purpose will enable manufacturers to carry this out with greater
accuracy, control and reproducibility. By infusing QbD supported by PAT
into processes, it is possible to significantly reduce manufacturing
times and costs, while ensuring the products created are of a quality
and consistency required by regulatory bodies, healthcare practitioners
and patients alike.
Case Study: Effervescent Tablet Formulation
The
formulation of an effervescent tablet that was examined for this case
study involved a two-step blending process. Firstly, the API was mixed
with approximately 50% of the excipients. In the second stage, the
remaining 50% of the excipients were added and blended with the mixture.
The blending times for these steps had traditionally been based
on experience and commonly used settings, rather than verified data
based on scientific investigation. The focus of this case study was to
perform an examination of the mixing process, and to determine whether
or not blending times could be adjusted to reduce production time (and
therefore cost) without impacting BU (and, by extension, product
quality).
Methods
An NIR spectrometer mounted on the blend container was used to record spectra. In order to avoid possible
interference from the excipient during data analysis, specificity was
ensured by recording spectra of both the API alone and the matrix
formulation alone (meaning the entire formulation mixture minus the
API).
Calibration samples were recorded at a range of API
concentrations and also analysed via HPLC. Standard normal variate was
utilised for spectra pre-treatment. A quantitative model was employed
using partial least squares with the spectral data and HPLC assay
results as reference values. Making use of this model, API concentration
was predicted for the production data and plotted against the blending
time (number of revolutions). A low variability in the predicted assay
values is indicative of blend homogeneity.
Results
A
good correlation for the calibration plot was achieved and the
associated production data clearly highlight both blending steps in this
case. The data from the quantitative model show that homogeneity for
the first blending step is achieved after approximately 40 revolutions.
After adding the second half of the excipients to the mixture, BU is
achieved following another 35 revolutions; thereafter, no significant
improvement is seen. The predicted concentrations from the second
blending step correlate well with the label claim, as confirmed by
laboratory quality control analysis of the final product.
The
data presented here show that NIR spectroscopy is highly sensitive, to
the point that it can even be used to differentiate the direction of
container rotation. Based on the analysis of these results, it was
determined that the blending time could be shortened by approximately 50
revolutions without affecting product quality.
Acknowledgements
The author would like to thank Dr Martin Koeberle and Wolfgang Schiemenz for their significant contributions to this article.
References
1. Yu LX et al, Understanding pharmaceutical Quality by Design, AAPS J
16: pp771-783, 2014
2. Sulub Y, Konigsberger M and Cheney J, Blend
uniformity end-point determination using near-infrared spectroscopy and
multivariate calibration, J Pharm Biomed Anal 55: pp429-434, 2011
3.
Sulub Y et al, Real-time on-line blend uniformity monitoring using
nearinfrared reflectance spectrometry: A noninvasive off-line
calibration approach, J Pharm Biomed Anal 49: pp48-54, 2009
4. Puchert T
et al, A new PAT/QbD approach for the determination of blend
homogeneity: Combination of on-line NIRS analysis with PC Scores Distance Analysis (PC-SDA), Eur J Pharm Biopharm 78: pp173-182, 2011
5.
Pharmaceutical quality for the 21st century: A risk-based approach,
Progress report. Visit: www.fda. gov/aboutfda/centersoffices/
officeofmedicalproductsandtobacco/cder/ucm128080.htm
6. ICH Guidelines. Visit: www.ich.org/ products/guidelines/quality/article/ quality-guidelines.html
7.
FDA Quality by Design for ANDAs: An example for immediate-relapse
dosage forms. Visit: www.fda.gov/ downloads/drugs/development
approvalprocess/howdrugsare developedandapproved/
approvalapplications/abbreviated newdrugapplicationandagenerics/
ucm304305.pdf
8. FDA-EMA extends pilot program of the QbD
parallel-assessment. Visit: www.fda.gov/drugs/drugsafety/ ucm388009.htm
9. FDA, Guidance for Industry: Powder Blends and Finished Dosage Units –
Stratified In-Process Dosage Unit Sampling and Assessment. Visit:
www.pqri.org/blenduniformity/ imagespdfs/fdadraftguide.pdf
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