Excipient and Active Pharmaceutical Ingredient Vendor Qualification

The physical characterization of pharmaceutical excipients provides data that can be predictive in nature regarding the performance of final dosage forms such as tablets, capsules and transdermals. Some of this physical testing data, such as particle size for example, is generally provided by the manufacturer.

The specification data provided by a manufacturer relating to particle size may be broader than what is actually preferred for a specific product or process. In addition, other tests such as porosity, density or surface area may not be reported.

In certain cases, this data may provide a better understanding about the behavior of a specific material in a given process such as blending, flow and compression, or in a final dosage form such as dissolution, disintegration and bioavailability.

The ICH Q8, Q9, and Q10 guidelines outline Quality by Design, risk analysis, design space and control strategies. By implementing these guidelines regarding active pharmaceutical ingredients (APIs) and excipients, companies can gain more insight into their materials and the extent of their effect in a formulation.

Microcrystalline cellulose and lactose are common excipients that are used in solid oral dosage forms. Supplier-to-supplier or lot-to-lot variation in these materials may possibly give rise to unnecessary issues, especially when they form the bulk of a formulation.

In this study, lactose and microcrystalline cellulose were subjected to a series of tests to show the level of consistency of the materials. In order to replicate a raw material vendor qualification study, three lots of each material were examined. DFE Pharma provided the following excipients:

  • Spray-dried lactose (SuperTab 11SD)
  • Anhydrous lactose (SuperTab 21AN)
  • Microcrystalline cellulose (Pharmacel 101)

All materials were analyzed for the following characteristics:

  • Porosity by mercury intrusion porosimetry on the AutoPore IV 9500
  • Skeletal or true density by helium pycnometry on the AccuPyc 1340
  • Particle size distribution by laser light scattering on the Saturn DigiSizer II
  • BET specific surface area utilizing krypton gas on the ASAP 2420 surface area analyser

Table 1 and 2 below lists the data produced for each test given above. Based on the internally developed specification or application of a particular company, the data could be used to reveal lot-to-lot similarity or may reveal that further controls are required to make sure that the material is suitable for a particular application.

Table 1. Lot with particle size (volume distribution).

Material Lot Particle Size (Volume Distribution)
Mean D90 D50 D10
SuperTab 21AN 10678881 132.874 299.980 118.163 1.286
10640579 123.902 278.460 111.046 1.184
10680069 137.314 298.012 128.024 1.399
Mean 131.363 292.151 119.078 1.290
%RSD 5.2 4.1 7.2 8.3
SuperTab 11SD 10614997 59.168 117.334 53.639 4.146
10643209 67.634 124.826 65.090 11.091
10641963 69.883 136.195 64.786 7.324
Mean 65.562 126.118 61.172 7.520
%RSD 8.6 7.5 10.7 46.2
Pharmacel 101 00100016 51.809 98.606 49.353 7.824
00100014 55.109 103.303 53.231 9.020
00100018 57.587 105.306 56.397 11.028
Mean 54.835 102.405 52.994 9.291
%RSD 5.3 3.4 6.7 17.4

 

Table 2. Data generated for density, porosity, and surface area.

Material Lot Density (g/cc) Porosity (%) Surface Area (m2/g)
SuperTab 21AN 10678881 1.5821 8.5783 0.3490
10640579 1.5810 8.5917 0.3442
10680069 1.5798 11.1114 0.3452
Mean 1.5810 9.4271 0.3461
%RSD 0.07 15.5 0.73
SuperTab 11SD 10614997 1.5389 3.4083 0.2172
10643209 1.5391 2.8102 0.2207
10641963 1.5384 3.0303 0.1892
Mean 1.5388 3.0829 0.2090
%RSD 0.02 9.8 8.26
Pharmacel 101 00100016 1.5495 18.6942 1.3805
00100014 1.5545 16.3986 1.3345
00100018 1.5527 16.9754 1.3792
Mean 1.5522 17.3561 1.3647
%RSD 0.16 6.9 1.92

 

There is no generic right or wrong data set for all products or processes. The data depends on a particular application. For instance, when qualifying a new supplier of raw materials, it is important to ensure the data shows similarity, or a tighter control may be required for a key parameter due to unnecessary effects on a product performance characteristic.

The data thus produced for this analysis is more thorough when compared to dependency on vendor specifications alone. This data, in conjunction with matching product performance data, can improve the level of confidence that the product being produced will be better in terms of performance, consistency and robustness after administering it to the patient.

Conclusion

In pharmaceutical formulations, the monitoring of raw materials is a key part of the overall control strategy. An improved understanding of physical characteristics can be very useful when developing control strategies or a design space.

This ensures that quality is integrated into products and/or processes and together with final product performance, can aid in detecting key quality attributes and critical process parameters of initial materials and final dosage forms.

In this empirical study, the additional testing has shown that a more comprehensive examination of materials may help guarantee material consistency from a specific supplier, offer a series of testing that can be carried out to qualify new material suppliers and may eventually create data that could be predictive in nature with regard to the performance of a product or process. If this knowledge base is built around the starting materials, many cases of unwanted process or product performance that may occur could be understood and more easily resolved with additional tools in the “toolbox”.

This information has been sourced, reviewed and adapted from materials provided by Micromeritics Instrument Corporation.

For more information on this source, please visit Micromeritics Instrument Corporation.

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