Analytical techniques in determining on line blend uniformity in powder technology

1. Analytical techniques in
determining on line blend
uniformity in powder technology
By : Debanjan Das
{DISCLAIMER: Certain details and/or graphics used here,
pertaining to proprietorship of the project had been altered.
This exercise is solely for enhancement of knowledge-base and
not for any “for-profit” venture}

2. Reasons for this study
 Lack of adequate potency and / or content
uniformity has been the number one product
quality reason for the recall of marketed
solid dosage forms
 The lack of blend homogeneity is one of
several possible reasons that may
contribute potency or content uniformity in
marketed products

3. Reasons (cont.)
 Some processes are always prone to
problems with blending, segregation, and
flow of powders, which are developed and
introduced into the production, ultimately
resulting in varying degrees of content
uniformity issues
 Difficulties encountered in physically
sampling a stationary powder bed using
sample thieves, which have been
demonstrated to be very prone to sampling
errors

4. A model blending operation

5. Problems during blending
 Typically, four to six powder components
are added into the top of the blending
machine
 Ideally, the rotation stops when the mixture
is uniform, usually after 10 to 30 min
 No method currently exists that detects
uniformity during the blending process, and
as a result the true optimum endpoint rarely
is realized

6. Problems (cont.)
 If blending is excessive, then the
particles will segregate (demix or
deblend) on the basis of particle size
and mass
 Temperature increases that occur
during blending can damage some of
the sensitive components

7. Validation Issues
 Blend uniformity is a function of both the
formulation and the mixing action
 Once the formulation is optimized from a
theoretical process standpoint, blend
uniformity then must be validated during
piloting and scale-up
 Validation involves stopping the blender,
extracting a sample, and analyzing the
active-ingredient content

8. Validation (cont.)
 After the blend time has been derived and
adopted in production, usually it is
reevaluated only if poor content uniformity of
the tablets has been detected
 Variations from the determined ideal blend
time result from the influence of factors such
as environmental temperature and humidity,
feedstock grade, and component particle
distribution and blender type, all of which
may vary from batch to batch

9. FDA perspective
 From FDA’s perspective, poor uniformity
poses potential threats to public health
 The Current Good Manufacturing Practices
(cGMPs) as described in 21CFR 211.110
require in-process controls to assure the
uniformity and integrity of each batch of
drug products
 The required in-process control procedures
include testing to evaluate the adequacy of
mixing to assure uniformity and
homogeneity

10. FDA (cont.)
 In 1999, FDA published a draft guidance to
provide recommendations on establishing
in-process acceptance criteria for blend
uniformity analysis to applicants with ANDA
(Abbreviated New Drug Application)
products
 Industrial feedback to this draft guidance
eventually led to industry, academia, and
the FDA working together in the Blend
Uniformity Working Group (BUWG) of the
Product Quality Research Institute

11. Blending process & QC steps
 A typical analytical procedure for solid
dosage forms is to obtain a sample batch of
the tablets or capsules from a production
line to the laboratory
 The tablets are grounded or the capsules
emptied, and some form of chemical and
spectrometric testing proceeds
 This process is time-consuming and the
tablets or capsules are destroyed

12. QC (cont.)
 If the analysis fails to satisfy the criteria for
acceptance, then no remedial action can be
taken and the batch can be wasted
 With the technological improvement in
tablets production at higher rates (3000
tablets a minute) and higher potency drugs
(at less than 1% active ingredient w/w),
there is a need for faster and higher
sensitive sensor for on-line analysis

13. Blending Process & QC steps

14. Offline methods-Sampling
errors
 Homogeneity as measured by sampling with a thief
and off-line analysis of the powder mixture
 Influenced by the skill of the operator and often
provides false representation of the sample due to
desegregation and disruption of the powder bed
during sampling and transport
 Blending validation is mandatory, due to the FDA’s
1996 proposal to amend the GMP regulations and
commercial-batch final blends need to be tested
routinely for blend homogeneity

15. Overview of on & offline
testings

16. Time comparison
(red bar-offline; green bar-online)

17. Online testing-Need of the
hour
 As evident, the on-line testing methods will
be the number one tool for saving work
hours and generating large revenue
 Eventually help in better confidence limits in
finished products thus preventing
unfortunate recalls
 Essential supplement to usual FDA
approved HPLC, Spectroscopy, Pulse
Polarography, Ion-pair chromatography etc

18. Latest techniques
 A. Monitoring blend uniformity with
Effusivity technique.
 B. Monitoring blend uniformity by NIR
spectroscopy and stream sampling
 C. Monitoring blend uniformity by NIR
by pattern reconition algorithm by
Bootstrap and Chi-Square methods
 D. Monitoring blend uniformity with light
induced fluorescence technique.

19. Monitoring Blend uniformity
with Effusivity technique
 The effusivity of a material is sometimes
called thermal inertia
 is the square root of the product of the
thermal conductivity, density and heat
capacity
 If the effusivity of a material is high—like
with ceramic, the interfacial temperature is
lower
 If the effusivity is lower—like with wood, the
interfacial temperature is higher

20. Effusivity principle
 This is why a wood floor feels warmer than a
ceramic floor, and why a carpeted floor feels
warmer still
 When touched, wood does not draw any
heat from our hand, while metal does
 ET is an automated hand that can
differentiate between slight differences on
which uniformity is constantly measured

21. ET & blend uniformity
 Concept of consistency measurement
through the use of relative measurements
between locations and correlating that result
to the degree of powder blend uniformity
 Active ingredient content is not the primary
measure, but rather the overall powder
consistency can be determined by relative
degree of heat transfer
 Just like our hand can differentiate between
wood and metal by touch, the blend
uniformity monitoring sensors can
differentiate between lactose, avicel and
calcium carbonate.

22. ET advantage
 The blend uniformity monitoring
sensors operate exactly like a hand
which offer the following advantages
• -pharmaceutical /powder blend
monitoring in real time
• -uniformity without thieving
• -retrofitable onto existing equipment
• -both on and offline testings

23. ET for offline testing
 By measuring samples extracted from
a blender using the thieving technique
 The properties of the powder from
different locations can be compared
 The absolute value measured is not
critical to the analysis, but rather it is
the variation between results that is
indicative of uniformity

24. ET for online testing
 Sensors can be easily retrofitted into
blenders
 Can be removed for replacement or
cleaning
 During a typical 15 minute blend, 5-10
tests can be conducted
 Data transmission could be batch
(during stop), continuous, or wireless

25. Placing the sensor probes

26. Interpreting probe results
 Differing measurements from sensors at
various locations indicates an initial non-
uniform blend that yields a high relative
standard deviation (RSD)
 As time progresses and uniformity is
achieved, the measurements will converge
and the RSD will decrease
 The RSD is a proxy for blend uniformity and
will approach zero for a perfect mixture
 The absolute measurement is not critical to
the process.

27. Correlation of effusivity with
time of blend

28. Blend time determination
 Multiple sensors are strategically placed in a
blender with measurements taken every few
minutes
 The results at each of these multiple sensor
locations will be closer, until a point of
smallest deviation is reached in the results
 Beyond that point, de-mixing or de-blending
will occur and the results will start to
separate

29. Case study of a formulation
 The powder sample was placed in a
container so that the material overflowed
 The probe sensor was inverted and placed
in contact with the powder, and a weight
was placed on top of the sensor
 The weight and the overfilled container
ensured that the packing was consistent

30. Placing of the powder

31. Component sensitivity-spanned
from 180 to 600 Ws1/2/m2K

32. Blend time

33. Blend uniformity testing with
Near Infrared Spectroscopy
 NIR is a useful analytical tool for both
qualitative and quantitative analyses
 Virtually every major pharmaceutical
company in Europe and the US has begun
to experiment with spectroscopic techniques
for process control
 Quicker than high performance liquid
chromatography or by UV spectroscopy

34. NIR- advantages
 Most pharmaceutical active ingredients and
excipients absorb near-IR radiation
 Complement the assay for the active
ingredient by providing homogeneity
information regarding all mixture
components
 Direct, quick, non-destructive technique for
assessing powder blend homogeneity could
be of great value in minimizing the sample
preparation and assay time associated with
traditional blend analysis procedures

35. Advantages (cont.)
 Viable analytical technique for the
evaluation of pharmaceutical powder
blends
 On-line monitoring systems using
NIRS are an alternative to the use of
sample thieves
 Offers the possibility of remote
sampling with fiber optic probes

36. Remote sensing, fiber optic
probes

37. NIR principle
 NIR spectral analysis is similar to other
spectrophotometric methods in that light
energy from a controlled source is directed
at a sample
 Near-infrared light spans the 800 to 2,500
nm range
 typically is used for measuring organic
functional groups, particularly C-H, O-H, N-
H and C=O

38. Principle (cont.)
 A detector measures the spectral
absorbance or reflectance of a sample
 Identification involves comparing this
unknown spectrum to a reference spectrum
 The differences between the unknown and
the reference spectrum are then evaluated
according to given criteria and a decision is
made on the identity of the unknown

39. NIR with stream sampling
 Stream sampling is an alternative to the use of
sample thieves
 Done by capturing the blend in stainless steel cups
as it flowed from the bottom of the blender
 It follows Allen’s "golden rules" of powder sampling,
which state that a powder should always be
sampled when in motion, and that sampling should
be done in small increments of time throughout the
entire powder stream rather than at the same pre-
selected sites at all times

40. Advantages of stream
sampling
 More samples can be obtained than by thief
sampling, which is limited by the difficulty of
obtaining the samples and possible changes
in the powder distribution as the thief is
inserted
 Stream sampling takes advantage of a
process that has to occur, as tablet
compression requires the flow of the blend
from a hopper or bin located over the
compressing machine

41. Advantages (cont.)
 Indicate segregation problems related to the
emptying of the blender - problems that thief
sampling is unable to pinpoint
 Does not show preferential sampling or
segregation of the powder blend by sample
thieves
 Implementation does not require a
significant financial investment

42. Advantages (cont.)
 Small pharmaceutical companies may be unable to
invest in on-line monitoring systems and the
specialized personnel needed to implement and
validate them
 Complements the on-line monitoring systems by
evaluating the blend one step beyond the blending
process as a hopper or bin is emptied
 As blenders increase in size, the thief handling
operation becomes more difficult, since it is
necessary to collect samples several feet below the
powder bed surface

43. Certain limitations
 Not able to target locations that are
suspected of providing poor blending
 Application of stream sampling is limited in
the final formulation scale up and
optimization process
 However, combination of NIR with stream
sampling results in the most powerful tool
for determining blend uniformity

44. Instrumentation assembly

45. Constructing a model
calibration model
 A primary concern in the development of
calibration model was whether the near
infrared radiation contacted the majority of
the powder blend
 Diffuse reflectance can be affected by many
factors, such as the sample packing, the
particle size, and the crystallinity of the
material
 Excipients such as talc has strong,
interfering bands which has to be exactly
calibrated.

46. Blend spectra- talc (top),
ibuprofen,lactose (middle) &
ibuprofen,talc (bottom)

47. Spectral analysis
 The bottom spectrum showed that a very
weak band for the talc superimposed over
the blend spectrum, showing that the near
infrared radiation reaches the bottom
layered talc
 It is not possible to determine whether the
entire blend was sampled by the near
infrared radiation, but it was confirmed that
the blend near the bottom of the cup can be
sampled by the near infrared radiation

48. Correlation between drug
concentration and NIR spectra

49. Spectra of samples collected
in varied times

50. Optimization of blending time

51. Monitoring blend uniformity by
NIR by pattern recognition
algorithm
 Blend homogeneity and optimal mixing
times can be quatitatively determined
using a single and multiple bootstrap
algorithms and ususal chi-square
analysis
 BEST ( Bootstrap Error adjusted
Single Sample Technique)

52. BEST technique
 It represents a type of analytical procedure
to operate in high speed parallel or vector
mode required of pattern recognition tests
involving a thousand of samples
 provide both quantitative and qualitative
analyses of intact products
 The BEST starts by treating each
wavelength in a spectrum as a single point
in multidimensional space or the
“hyperspace”

53. BEST (cont.)
 Samples with similar spectra map into
clusters of points in similar regions of
hyperspace, with large cluster size
corresponding to samples with greater
intrinsic variability, which in-turn leads to
identification of individual concentrations of
ingredients present.
 Hence, BEST develops an estimate of the
total sample population using a small set of
known samples

54. BEST (cont.)
 Where the single sample BEST algorithm
provided the qualitative analysis of a single
test sample, modified BEST algorithm
provides a test that uses multiple test
spectra to detect false samples or sub
clusters well within the SD limit of the
sample set
 The accurate detection of sub clusters
allows the determination of very small
changes in component concentration

55. Optimizing blend time from
modified BEST QQ plot

56. Chi-squared analysis
 For each time point, the pooled variance of
the Near-IR absorbance values at individual
wavelengths are calculated as the weighted
average of the variances, where weights
formed the degrees of freedom
 It provides rapid analysis of multiple powder
blends along with determination of blending
time

57. Optimization of blend time directly
from a multiple blend study

58. Problems encountered-
Spectral Shift
 Caused by magnesim stearate present in
the formulation of the blend
 During the mixing process, the lubricant
particles such as the magnesium stearate,
first adsorb on to the surface of individual
powder particles or granules. When mixing
continues, they distribute more uniformly
upon the granule surface following
delamination or deagglomeration
mechanisms
 It affects the surface characteristics of the
powder blend and causes the spectral shift

59. NIR chemical image of an
uniform blend distribution

60. NIR chemical blend of a non
uniform blend distribution

61. Light Induced Fluorescence
(LIF)
 This is the latest technology that monitors
the progress of powder homogeneity non-
invasively and in real-time
 Operation of LIF involves irradiating powder
samples on-line at a suitable wavelength for
fluorescent excitation and evaluating the
emission at another wavelength

62. LIF - Advantages
 Drugs in the marketplace suggests that a
majority of them are likely to fluoresce when
excited at the proper wavelength
 Rapid, on-line method allows one to
examine the details of blending kinetics and
the effect of blending
 Analysis is rapid and usually on the order of
microseconds

63. LIF - Advantages
 If a continuous light source is used, then the
limit to data acquisition is the limit of
computing speed
 Also determines the effect of blending
conditions, such as blender type, physical
particle characteristics, and order of
component addition
 Apart from pharmaceutical powders, this
technology is applicable for all types of
powder mixing processes in other industries

64. LIF sensor assembly

65. LIF signal and mixing states

66. Mixing kinetics
 Changes in bulk density of the powders in the
blender corresponded to a proportional change in
LIF signal
 For an evenly mixed sample, there will be more API
for each unit surface volume excited by the laser
beam with increasing packing density
 Bulk-density variation during the blending process
hence may elevate background noise
 It is favorable to consider monitoring blend
homogeneity within the blender at a location where
bulk-density changes were minimal

67. Optimizing design assembly
 The efficiency of LIF sensor rests in the
laser power source, the detector
sensitivities, and the excitation wavelength
can independently control signal intensity
 The primary process variables that impacted
the signal quality are void volumes and
differences in bulk densities during the
mixing
 Selecting a location for data acquisition
close to the bottom of the vessel where the
powder bulk density is relatively constant

68. Determination of blend time
 Blend homogeneity is established when the
LIF signal at steady state is the same from
one turnover of powder to the next for each
mixing rotation
 The monitoring process is considered as a
snapshot of the powder content at each
rotation
 The signal derived from each snapshot is a
quantitative representation of the number of
fluorescent particles distributed within that
area of analysis

69. Blend time (cont.)
 Changes in that number of fluorescent
particles within that area of analysis will
result in changes in fluorescence signals
that indicate a non-homogeneous state
 Any presence of a dead spot would result in
an overall change in the API concentration.
This change in concentration would result in
a change in signal when steady is
established

70. Blend time (cont.)
 The application of LIF to non-invasively monitor
blend homogeneity during dry pharmaceutical
powder mixing allows one to acquire real-time data
on kinetics and the endpoint of mixing
 This approach eliminates errors introduced by the
use of thief sampling and off-line assay techniques
 both process and product verification non-invasively
and in real-time for blending of dry active
pharmaceutical ingredient with excipients
 A portable version is also available

71. LIF signals Vs rotations

72. LIF detection unit

73. Best advantage- quick
analysis of intact tablets
 From the fluorescence emission of the excited
sample, information about the sample’s surface
constituent makeup can be measured
 Large number of tablets can be quickly and easily
analyzed
 Specialized applications with a continuous
monochromatic light source can increase the
detection rate several fold
 Determining content uniformity, not only from
randomized drawn samples but also during on-line
production for every tablet

74. Discussions
 Manufacturing productivity is directly related
to analytical efficiency: a faster answer
leads to a faster decision to move the
process forward
 For industrial processing regimens, an
analytical productivity improvement should
provide not just a reduction in process cycle
time but an increase in process knowledge
 This can be obtained through the acquisition
of high-density information using real-time
methodologies

75. Discussions (cont.)
 But the use of such recent analytical on-line
techniques is still not widespread among
pharmaceutical companies
 Multivariate system costs have settled into
the $50,000 to $100,000 range. Even
though there can be significant cost
justifications, for many smaller companies
this is still a considerable investment

76. Discussions (cont.)
 In the light of this apparent confusion of whether or
not to embrace newer technologies, PQRI
forwarded its first recommendation to FDA for
review in March 2002
 Shortly after receiving the institute’s report, FDA
announced that it was withdrawing a controversial
1999 draft guidance on blend sampling
 However, PQRI’s Blend Uniformity Working Group
(BUWG) continues to address the gap between
scientific principles and regulatory policy related to
blend uniformity analysis and content uniformity of
solid oral dosage forms

77. Discussions (cont.)
 In an agreement between FDA and PQRI,
the FDA will evaluate the recommendation
and either adopt it or, if it chooses not to
adopt it, provide a scientific explanation to
PQRI where the recommendation is lacking
 Strategically, for businesses that are guided
either by industrial standards or regulatory
agencies, the incorporation of new
technology can augment the benchmarks
associated with running an operation

78. Discussions (cont.)
 Few companies take the risk to be first, preferring
instead to wait until the technique is mature and
accepted. For those willing to take the risk,
knowledge uncovered with the use of new
technology can often lead to tactical if not strategic
processing advantages
 Those companies leading the analytical technology
curve will have not only the ability to guide and
shape future regulatory policy but also acquire the
many benefits associated with enhanced
productivity

79. Conclusions
 Technique of on-line blend uniformity
testing, will no longer stay as an
“orphan-technology”, but will emerge
as the most powerful tool in powder
technology in the coming decades

80. Thank you.