Journal of Pharmaceutical Innovation

, Volume 13, Issue 2, pp 188–196 | Cite as

Estimating Number of PPQ Batches: Various Approaches

  • Naseem Ahmad Charoo



The FDA’s process validation guidance 2011 has rightly resulted in discontinuing the “one size fits all” practice. The guidance aligns process validation with quality by design and quality risk management guidelines. However, the process validation guidance has thrown a challenge with respect to determining the statistically appropriate number of batches for process performance qualification (PPQ) stage. This study reviews various approaches for estimating the number of PPQ batches, and their merits and limitations. Additionally, a knowledge factor-based method in which residual risk level is related to the knowledge factor by a probability scale is proposed.

Methods and Results

The risk-based methods assign a confidence level to unit processes based on the risk posed to critical quality attributes of the product. The level of product understanding and residual risk would determine the number of PPQ batches required for process validation. The knowledge factor-based method like other Bayesian methods provides an opportunity to incorporate knowledge gained during product/process development and scale up studies for estimating PPQ batch numbers. The number of batches required using this method are 6, 12, and 15, respectively, for low-, moderate-, and high-risk processes with corresponding knowledge factors of 0.1, 0.5, and 0.9.


Greater understanding and knowledge of product would reduce the requirement of PPQ batches remarkably. On the other hand, the higher residual risk level indicates knowledge gaps in product understanding; consequently, higher number of PPQ batches would be required to gain confidence in the product and the process before commercialization.


Process validation Process performance qualification Sample size Quality by design Quality risk management Number of batches 


Compliance with Ethical Standards

Conflict of Interest

The author declares that he has no conflicts of interest.


  1. 1.
    Food and Drug Administration CDER. Final report on pharmaceutical cGMPs for the 21st century—a risk-based approach, 2003. Available from: Htm. Accessed 14 May 2017.
  2. 2.
    Food and Drug Administration CDER. Guidance for industry Q8 (R2) pharmaceutical Development, 2009. Available from: Accessed 2nd Dec 2017.
  3. 3.
    FDA (CDER, CBER, CVM). Guidance for industry: process validation: general principles and practices, 2011. Available from: Accessed 14 May 2017.
  4. 4.
    International Conference on Harmonization., ICH Q9- Quality risk management. EMA/INS/GMP/79766/2011, 2011. Available from: Accessed 14 May 2017.
  5. 5.
    Charoo NA, Shamsher AA, Zidan A, et al. Quality by design approach for formulation development: a case study of dispersible tablets. Int J Pharm. 2012;423(2):167–78. Scholar
  6. 6.
    Charoo NA, Ali AA. Quality risk management in pharmaceutical development. Drug Dev Ind Pharm. 2013;39(7):947–60. Scholar
  7. 7.
    Yu LX, Amidon G, Khan MA, Hoag SW, Polli J, Raju GK, et al. Understanding pharmaceutical quality by design. AAPS J. 2014;16(4):771–83. Scholar
  8. 8.
    Charoo NA, Cristofoletti R, Kim SK. Integrating biopharmaceutics risk assessment and in vivo absorption model in formulation development of BCS class I drug using the QbD approach. Drug Dev Ind Pharm. 2017;43(4):668–77. Scholar
  9. 9.
    Bryder M, Etling H, Fleming J, Hu Y, Levy P. Topic 1—stage 2 process validation: determining and justifying the number of process qualification batches, ISPE discussion paper: PV stage 2, number of batches (version 2), 2014. Available from: Accessed 2nd Dec 2017.
  10. 10.
    Wiles F. Risk-based methodology for validation of pharmaceutical batch processes. PDA J Pharm Sci Tech. 2013;67(4):387–98. Scholar
  11. 11.
    Wu C, Kuo HL. Sample size determination for the estimate of process capability indices. Inform Manag Sci. 2004;15(1):1–12.Google Scholar
  12. 12.
    Bissell AF. How reliable is your capability index? Applied Stat. 1990;39(3):331–40. Scholar
  13. 13.
    Kushler RH, Hurley P. Confidence bounds for capability indices. J Qual Tech. 1992;24(4):188–95.CrossRefGoogle Scholar
  14. 14.
    Kleyner A, Elmore D, Boukai B. A Bayesian approach to determine test sample size requirements for reliability demonstration retesting after product design change. Qual Eng. 2015;27(3):289–95. Scholar
  15. 15.
    Charoo NA, Durivage M, Rahman Z, Ayad MH. Sample size for tablet compression and capsule filling events during process validation. J Pharm Sci. 2017;106(12):3533–8. Scholar
  16. 16.
    Yang H. How many batches are needed for process validation under the new FDA guidance? PDA J Pharm Sci Tech. 2013;67(1):53–62. Scholar
  17. 17.
    Guo H, Pohl E, Gerokostopoulos A. Determining the right sample size for your test: theory and application. 2013 Reliability and Maintainability Symposium, January, 2013.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Zeino Pharma LLC, Khalifa Industrial Zone Abu DhabiAbu DhabiUnited Arab Emirates

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