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« SPECTROSCOPY of validation samples.16 In this way a spectrum with more

noise may be obtained and its effect of the predictions determined. This approach may also be part of the robustness study.

The model could then be used to predict samples from a manufacturing facility. The samples from the manufacturing facility would then be analyzed by an HPLC system within the QC lab of the manufacturing facility. Researchers at the Universitat Autònoma de Barcelona have provided examples of this approach.17,18

Thus, the least

expensive gravimetric calibration samples are prepared first. The more expensive and laborious HPLC testing was performed after a number of test sets indicated that the method should perform well. The QC laboratory at the manufacturing facility is usually separate from the laboratory that developed the calibration model. The QC laboratory provides an “independent” assessment, even though it still depends on the calibration and use of the analytical balance. Independence stops at the analytical balance, all results depend on the proper use of analytical balances based on absolute calibration with internationally recognized weight standards.13

The end goal is still clearly expressed in the current Good Manufacturing Practices (cGMPs). Section 211.194 a2, specifies that “The suitability of all testing methods used shall be verified under actual conditions of use.” A previous publication indicated “Sample independency means that samples are not prepared under the same conditions as the calibration set samples. Validation samples should come from the process that will be monitored and be prepared with excipient and API batches that differ from those used in the calibration set.”4

This definition is consistent with the expectations of the FDA and EMA documents. Final Comments

Validation efforts should not be restricted by the definition of the calibration test set given in the EMA and FDA documents. The calibration test set is seen as an initial check of the validity of the model, and the application of resampling statistics to cross-validate the performance of the model for the purposes of optimization. Validation may involve several test sets (validation samples) to progressively challenge the model,3,4

in a strategy that does not fit the calibration test set definition described in the EMA and FDA documents.

The EMA and FDA documents are important summaries of the progress made in developing non-destructive analytical methods for pharmaceutical processes. These documents are worthy of further discussion. Hopefully this article contributes to this needed discussion.


The work supported by National Science Foundation through Grant NSF-EEC0540855 was the seed for many helpful discussions that resulted in this article.


1. U.S. Department of Health and Human Services FDA. Development and Submission of Near Infrared Analytical Procedures Guidance for Industry Draft Guidance. 2015.

2. European Medicines Agency. Guideline on the use of Near Infrared Spectroscopy (NIRS) by the pharmaceutical industry and the data requirements for new submissions and variations. 2014:28.

3. Esbensen KH, Geladi P. Principles of Proper Validation: use and abuse of re-sampling for validation. J. Chemometrics. 2010;24(3-4):168-187.

4. Romañach R, Román-Ospino A, Alcalà M. A Procedure for Developing Quantitative Near Infrared (NIR) Methods for Pharmaceutical Products. In: Ierapetritou MG, Ramachandran R, eds. Process Simulation and Data Modeling in Solid Oral Drug Development and Manufacture: Springer New York; 2016:133-158.

5. Corredor C, Lozano R, Bu X, et al. Analytical Method Quality by Design for an On-Line Near- Infrared Method to Monitor Blend Potency and Uniformity. J Pharm Innov. 2015;10(1):47- 55.

6. Kramer R. Chemometric Techniques for Quantitative Analysis. Taylor & Francis; 1998.

7. Càrdenas V, Blanco M, Alcalà M. Strategies for Selecting the Calibration Set in Pharmaceutical Near Infrared Spectroscopy Analysis. A Comparative Study. J Pharm Innov. 2014;9(4):272-281.

8. Bondi RW, Jr., Igne B, Drennen JK, 3rd, Anderson CA. Effect of experimental design on the prediction performance of calibration models based on near-infrared spectroscopy for pharmaceutical applications. Applied spectroscopy. 2012;66(12):1442-1453.

9. Sulub Y, Wabuyele B, Gargiulo P, et al. Real-time on-line blend uniformity monitoring using near-infrared reflectance spectrometry: a noninvasive off-line calibration approach. J. Pharm. Biomed. Anal. 2009;49(1):48-54.

10. Blanco M, Romero MA, Alcala M. Strategies for constructing the calibration set for a near infrared spectroscopic quantitation method. Talanta. 2004;64(3):597-602.

11. Mark H, Ritchie GE, Roller RW, Ciurczak EW, Tso C, MacDonald SA. Validation of a near- infrared transmission spectroscopic procedure, part A: validation protocols. J. Pharm. Biomed. Anal. 2002;28(2):251-260.

12. Ritchie GE, Roller RW, Ciurczak EW, Mark H, Tso C, MacDonald SA. Validation of a near- infrared transmission spectroscopic procedure. Part B: Application to alternate content uniformity and release assay methods for pharmaceutical solid dosage forms. J. Pharm. Biomed. Anal. 2002;29(1-2):159-171.

13. Martens H, Naes T. Multivariate Calibration. Wiley; 1992.

14. Small GW. Chemometrics and near-infrared spectroscopy: Avoiding the pitfalls. TrAC Trends in Analytical Chemistry. 2006;25(11):1057-1066.

15. Xiang D, Berry J, Buntz S, et al. Robust calibration design in the pharmaceutical quantitative measurements with near-infrared (NIR) spectroscopy: Avoiding the chemometric pitfalls. Journal of pharmaceutical sciences. 2009;98(3):1155-1166.

16. Colon YM, Florian MA, Acevedo D, Mendez R, Romanach RJ. Near Infrared Method Development for a Continuous Manufacturing Blending Process. J Pharm Innov. 2014;9(4):291-301.

17. Blanco M, Bautista M, Alcala M. Preparing calibration sets for use in pharmaceutical analysis by NIR spectroscopy. Journal of pharmaceutical sciences. 2008;97(3):1236-1245.

18. Blanco M, Bautista M, Alcala M. API Determination by NIR Spectroscopy Across Pharmaceutical Production Process. AAPS PharmSciTech. 2008;9(4):1130-1135.

About the Author:

Rodolfo J. Romañach - is Professor of Chemistry and Site Leader for the Engineering Research Center for Structured Organic Particulate Systems at Mayagüez. His research involves near infrared and Raman spectroscopy and multivariate methods

for continuous improvement in manufacturing. | | 51


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