Analytical Techniques In Determining On Line Blend Uniformity In Powder Technology


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Analytical techniques in determining on line blend uniformity in powder technology

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Analytical Techniques In Determining On Line Blend Uniformity In Powder Technology

  1. 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. 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. 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. 4. A model blending operation
  5. 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. 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. 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. 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. 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. 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. 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. 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. 13. Blending Process & QC steps
  14. 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. 15. Overview of on & offline testings
  16. 16. Time comparison (red bar-offline; green bar-online)
  17. 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. 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. 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. 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. 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. 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. 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. 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. 25. Placing the sensor probes
  26. 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. 27. Correlation of effusivity with time of blend
  28. 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. 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. 30. Placing of the powder
  31. 31. Component sensitivity-spanned from 180 to 600 Ws1/2/m2K
  32. 32. Blend time
  33. 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. 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. 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. 36. Remote sensing, fiber optic probes
  37. 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. 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. 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. 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. 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. 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. 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. 44. Instrumentation assembly
  45. 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. 46. Blend spectra- talc (top), ibuprofen,lactose (middle) & ibuprofen,talc (bottom)
  47. 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. 48. Correlation between drug concentration and NIR spectra
  49. 49. Spectra of samples collected in varied times
  50. 50. Optimization of blending time
  51. 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. 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. 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. 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. 55. Optimizing blend time from modified BEST QQ plot
  56. 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. 57. Optimization of blend time directly from a multiple blend study
  58. 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. 59. NIR chemical image of an uniform blend distribution
  60. 60. NIR chemical blend of a non uniform blend distribution
  61. 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. 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. 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. 64. LIF sensor assembly
  65. 65. LIF signal and mixing states
  66. 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. 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. 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. 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. 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. 71. LIF signals Vs rotations
  72. 72. LIF detection unit
  73. 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. 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. 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. 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. 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. 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. 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. 80. Thank you.