Prediction of dissolution profiles by non-destructive near infrared spectroscopy in tablets subjected to different levels of strain

Hernández, Eduardo A. Lugo; Pawar, Pallavi; Keyvan, Golshid; Wang, Yifan; Velez, Natasha; Callegari, Gerardo; Cuitiño, Alberto M.; Michniak‐Kohn, Bozena; Muzzio, Fernando J.; Romañach, Rodolfo J.

doi:10.1016/j.jpba.2015.10.012

Cited by 58 publications

(23 citation statements)

References 27 publications

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“…Overall, it has been demonstrated that process simulation can enhance process development by applying material tracking at a defined mass flow rates. CM lines are typically equipped with PAT monitoring techniques, such as Near Infrared (NIR) (Alam et al, 2017;Fonteyne et al, 2012a;Hernandez et al, 2016;Järvinen et al, 2013;Khorasani et al, 2016) and…”

Section: Introductionmentioning

confidence: 99%

RTD-based material tracking in a fully-continuous dry granulation tableting line

Martinetz

Karttunen

Sacher

et al. 2018

International Journal of Pharmaceutics

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Continuous manufacturing (CM) offers quality and cost-effectiveness benefits over currently dominating batch processing. One challenge that needs to be addressed when implementing CM is traceability of materials through the process, which is needed for the batch/lot definition and control strategy. In this work the residence time distributions (RTD) of single unit operations (blender, roller compactor and tablet press) of a continuous dry granulation tableting line were captured with NIR based methods at selected mass flow rates to create training data. RTD models for continuous operated unit operations and the entire line were developed based on transfer functions. For semi-continuously operated bucket conveyor and pneumatic transport an assumption based the operation frequency was used. For validation of the parametrized process model, a pre-defined API step change and its propagation through the manufacturing line was computed and compared to multi-scale experimental runs conducted with the fully assembled continuous operated manufacturing line. This novel approach showed a very good prediction power at the selected mass flow rates for a complete continuous dry granulation line. Furthermore, it shows and proves the capabilities of process simulation as a tool to support development and control of pharmaceutical manufacturing processes.

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Section: Introductionmentioning

confidence: 99%

RTD-based material tracking in a fully-continuous dry granulation tableting line

Martinetz

Karttunen

Sacher

et al. 2018

International Journal of Pharmaceutics

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“…The NIR spectrum ranges from 700 to 2500 nm due to the vibration of C–H, O–H and N–H bonds. NIRS has been widely accepted in agriculture (Fernandez‐Espinosa, ), textile (Cleve et al ., ), pharmaceutical (Hernandez et al ., ) and petroleum industry (Alves & Poppi, ). In health sector, NIRS has been used for glucose monitoring in human serum (Goodarzi & Saeys, ), for the development of an early alert system for cerebral hypoxaemia (Cruz et al ., ) and for the measurement of muscle oxygenation (Olivier et al ., ).…”

Section: Introductionmentioning

confidence: 99%

Green method for determination of phenolic compounds in mung bean (Vigna radiata L.) based on near‐infrared spectroscopy and chemometrics

Meenu

Kamboj

Sharma

et al. 2016

Int J of Food Sci Tech

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Summary Mung bean grains exhibit high level of antioxidant activity due the presence of phenolic compounds. Near‐infrared spectroscopy (NIRS), in conjunction with chemometrics, was used to develop a rapid, nondestructive, chemical free and easy‐to‐use method to determine these compounds in sixty genotypes of mung bean. NIRS calibration curve with high‐performance liquid chromatography (HPLC) as reference method was used to determine phenolic compounds (catechin, chlorogenic acid, caffeic acid, p‐coumaric acid, t‐ferulic acid, vitexin, isovitexin, myricetin, quercetin and kaempferol). It was observed that partial least square regression (PLSR) model in the wavelength range of 1600–2500 nm with standard normal variate (SNV) and linear baseline correction (LBC) as preprocessing techniques can measure phenolic compound accurately (R2 > 0.987) with root‐mean‐square error less than 1.82%. This study shows that NIRS along with chemometrics is an accurate method to estimate the phenolic compounds rapidly and nondestructively.

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“…Validation elements specific to a predictive dissolution model include accuracy relative to the reference method and robustness. Linearity and accuracy can be demonstrated by the correlation coefficient R and the root mean square error, respectively, for an observed versus predicted fit of either (1) percentage dissolved at a specific time point or (2) dissolution profile nonlinear regression coefficients (59,64). To gauge model accuracy, a comparison of passing/failing acceptance criteria, the root mean square error of calibration and/or cross-validation, and the root mean error of prediction (using an independent validation set) can be reported.…”

Section: Common Practices In Developing In Vitro Predictive Dissolutimentioning

confidence: 99%

First-Principles and Empirical Approaches to Predicting In Vitro Dissolution for Pharmaceutical Formulation and Process Development and for Product Release Testing

Zaborenko

Shi

Corredor

et al. 2019

AAPS J

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This manuscript represents the perspective of the Dissolution Working Group of the International Consortium for Innovation and Quality in Pharmaceutical Development (IQ) and of two focus groups of the American Association of Pharmaceutical Scientists (AAPS): Process Analytical Technology (PAT) and In Vitro Release and Dissolution Testing (IVRDT). The intent of this manuscript is to show recent progress in the field of in vitro predictive dissolution modeling and to provide recommended general approaches to developing in vitro predictive dissolution models for both early- and late-stage formulation/process development and batch release. Different modeling approaches should be used at different stages of drug development based on product and process understanding available at those stages. Two industry case studies of current approaches used for modeling tablet dissolution are presented. These include examples of predictive model use for product development within the space explored during formulation and process optimization, as well as of dissolution models as surrogate tests in a regulatory filing. A review of an industry example of developing a dissolution model for real-time release testing (RTRt) and of academic case studies of enabling dissolution RTRt by near-infrared spectroscopy (NIRS) is also provided. These demonstrate multiple approaches for developing data-rich empirical models in the context of science- and risk-based process development to predict in vitro dissolution. Recommendations of modeling best practices are made, focused primarily on immediate-release (IR) oral delivery products for new drug applications. A general roadmap is presented for implementation of dissolution modeling for enhanced product understanding, robust control strategy, batch release testing, and flexibility toward post-approval changes.

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Prediction of dissolution profiles by non-destructive near infrared spectroscopy in tablets subjected to different levels of strain

Cited by 58 publications

References 27 publications

RTD-based material tracking in a fully-continuous dry granulation tableting line

RTD-based material tracking in a fully-continuous dry granulation tableting line

Green method for determination of phenolic compounds in mung bean (Vigna radiata L.) based on near‐infrared spectroscopy and chemometrics

First-Principles and Empirical Approaches to Predicting In Vitro Dissolution for Pharmaceutical Formulation and Process Development and for Product Release Testing

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