Near infra red spectroscopy as a multivariate process analytical tool for predicting pharmaceutical co-crystal concentration
Authors: Clive Wooda, Abdolati Alwatia, Sheelagh Halseyb, Timothy Gougha, Elaine Browna, Adrian Kellya, Anant Paradkara
Abstract:
In this study, the application of near-infrared (NIR) spectroscopy for predicting the concentration of two pharmaceutical co-crystals, namely 1:1 ibuprofen – nicotinamide (IBU-NIC) and 1:1 carbamazepine – nicotinamide (CBZ-NIC), was examined. A Partial Least Squares (PLS) regression model was developed for each co-crystal pair using a series of standard samples to create calibration and validation datasets, which were then utilized to build and validate the models. The accuracy and linearity of the models were assessed using parameters such as the root mean square error of calibration (RMSEC), root mean square error of prediction (RMSEP), and correlation coefficient.
The results demonstrated the successful creation of accurate PLS regression models for both co-crystal pairs. These models can be employed to predict the concentration of the co-crystal in a powder mixture containing the co-crystal and the active pharmaceutical ingredient (API). It was observed that the IBU-NIC model exhibited smaller errors compared to the CBZ-NIC model. This discrepancy could potentially be attributed to the complex spectra of CBZ-NIC, which may reflect the distinct arrangement of hydrogen bonding associated with the co-crystal in contrast to the IBU-NIC co-crystal.
Based on these findings, it can be inferred that NIR spectroscopy holds promise as a Process Analytical Technology (PAT) tool in various manufacturing methods involving pharmaceutical co-crystals. The data presented in this study will contribute to facilitating future offline and in-line NIR investigations concerning pharmaceutical co-crystals.
Keywords
Co-crystal; NIR spectroscopy; Partial Least Squares; Prediction; Active Pharmaceutical Ingredient; Process Analytical Tool
Citation: Clive Wood, Abdolati Alwati, Sheelagh Halsey, Timothy Gough, Elaine Brown, Adrian Kelly, Anant Paradkar, Near infra red spectroscopy as a multivariate process analytical tool for predicting pharmaceutical co-crystal concentration, Journal of Pharmaceutical and Biomedical Analysis http://dx.doi.org/10.1016/j.jpba.2016.06.010
Received: 11 March 2016, Revised: 5 June 2016, Accepted: 7 June 2016, Available: online 7 June 2016
Copyright: © 2016 Elsevier B.V. or its licensors or contributors.Open Access funded by Engineering and Physical Sciences Research Council.
Conclusion
The results demonstrate that NIR spectroscopy can be used to accurately distinguish between the individual components and the co-crystal form of two different pharmaceutical co-crystal pairs. Clear differences in the NIR spectra were observed when shown in the second derivative, and varying levels of chemometrics enabled the PLS regression to achieve relatively low calibration and validation error values. The findings suggest that NIR spectroscopy could be utilised as an accurate PAT tool for pharmaceutical co-crystal manufacturing methods and could aid understanding of the co-crystallisation process.
Acknowledgements
This work was funded by the Engineering and Physical Sciences Research Council, UK; grant code EP/J003360/1.
