In-line NIR monitoring of key characteristics of enteric coated pellets

• Published in: European journal of pharmaceutics and biopharmaceutics Vol. 88; no. 3; pp. 847 - 855
• Main Authors: Marković, Snežana, Poljanec, Ksenija, Kerč, Janez, Horvat, Matej
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01-11-2014
• Subjects: Chemistry, Pharmaceutical - methods; Chemometrics; Drug Implants - analysis; Drug Implants - chemistry; Enteric coated pellets; Hierarchical partial least squares; In-line near infrared spectroscopy; Meta-analysis; Process analytical technology; Spectroscopy, Near-Infrared - methods; Tablets, Enteric-Coated; Q2; FDA; MVDA; PAT; Hierarchical partial least squares; NIR-LAB; LOD2; LOD1; SG; PLS; EMA; NIRS; Enteric coated pellets; SNV; HPLC; LOD; R2; In-line near infrared spectroscopy; D1; D2; NIR-LHP; D3; ICH; PCA; Meta-analysis; Process analytical technology; PC; NIR; API; SEM; VIP; Chemometrics; SEP
• ISSN: 0939-6411; 1873-3441
• DOI: 10.1016/j.ejpb.2014.10.003

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•We develop in-line NIR models for the key characteristics of enteric coated pellets.•The residual solvent content can be accurately followed in real-time.•Pellet size distribution and coating film thickness can be accurately monitored.•Time-evolving spectral data is required for prediction of the acidic resistance.•We identify correlations of monitored pellet characteristics and enteric performance.
We describe the development of an in-line monitoring approach for the fluid-bed drying and coating steps for the production of enteric coated pellets by NIR. Our results show that key pellet characteristics can be monitored in-line. Likewise, the finished product acidic resistance is in excellent agreement to the in-line NIR predictions. Samples were collected at regular intervals and analyzed by several reference methods to characterize both process steps. In-line NIR models for pellets size sieve fractions, residual solvent content, and amount of coating layer have been constructed. Both the pellet coating layer amount and the in-vitro enteric performance demonstrate low variability which represents a challenge to the usual chemometric model development approach. To overcome this challenge a hierarchical PLS model for predicting acidic resistance was successfully constructed using time-evolving spectral data from 22 batches. Moreover, a novel multivariate meta-analysis of the PLS loadings of individual in-line models and the hierarchical PLS model has identified which pellet characteristics correlate most significantly with the observed enteric performance of the finished product. Additionally, the meta-analysis pointed toward the presence of further mechanisms unrelated to studied characteristics that also significantly influence the acidic resistance.