Process analytical technology case study: Part II. Development and validation of quantitative near-infrared calibrations in support of a process analytical ... View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2005-06

AUTHORS

Robert P. Cogdill, Carl A. Anderson, Miriam Delgado, Robert Chisholm, Raymond Bolton, Thorsten Herkert, Ali M. Afnan, James K. Drennen

ABSTRACT

This article is the second of a series of articles detailing the development of near-infrared (NIR) methods for solid dosage-form analysis. Experiments were conducted at the Duquesne University Center for Pharmaceutical Technology to demonstrate a method for developing and validating NIR models for the analysis of active pharmaceutical ingredient (API) content and hardness of a solid dosage form. Robustness and cross-validation testing were used to optimize the API content and hardness models. For the API content calibration, the optimal model was determined as multiplicative scatter correction with Savitsky-Golay first-derivative preprocessing followed by partial least-squares (PLS) regression including 4 latent variables. API content calibration achieved root mean squared error (RMSE) and root mean square error of cross validation (RMSECV) of 1.48 and 1.80 mg, respectively. PLS regression and baseline-fit calibration models were compared for the prediction of tablet hardness. Based on robustness testing, PLS regression was selected for the final hardness model, with RMSE and RMSECV of 8.1 and 8.8 N, respectively. Validation testing indicated that API content and hardness of production-scale tablets is predicted with root mean square error of prediction of 1.04 mg and 8.5 N, respectively. Explicit robustness testing for high-flux noise and wavelength uncertainty demonstrated the robustness of the API concentration calibration model with respect to normal instrument operating conditions. More... »

PAGES

e273-e283

Identifiers

URI

http://scigraph.springernature.com/pub.10.1208/pt060238

DOI

http://dx.doi.org/10.1208/pt060238

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1024889847

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/16353987


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