Abstract:
The use of near infra red spectroscopy to predict the concentration of two pharmaceutical co-crystals; 1:1 ibuprofen – nicotinamide (IBU-NIC) and 1:1 carbamazepine – nicotinamide (CBZ-NIC) has been evaluated. A Partial Least Squares (PLS) regression model was developed for both co-crystal pairs using sets of standard samples to create calibration and validation data sets with which to build and validate the models. Parameters such as the root mean square error of calibration (RMSEC), root mean square error of prediction (RMSEP) and correlation coefficient were used to assess the accuracy and linearity of the models. Accurate PLS regression models were created for both co-crystal pairs which can be used to predict the co-crystal concentration in a powder mixture of the co-crystal and the active pharmaceutical ingredient (API). The IBU-NIC model had smaller errors than the CBZ-NIC model, possibly due to the complex CBZ-NIC spectra which could reflect the different arrangement of hydrogen bonding associated with the co-crystal compared to the IBU-NIC co-crystal. These results suggest that NIR spectroscopy can be used as a PAT tool during a variety of pharmaceutical co-crystal manufacturing methods and the presented data will facilitate future offline and in-line NIR studies involving 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.
