Near infra red spectroscopy as a multivariate process analytical tool for predicting pharmaceutical co-crystal...

Area of Science:

• Pharmaceutical Science
• Analytical Chemistry
• Spectroscopy

Background:

• Pharmaceutical co-crystals are advanced drug delivery systems.
• Accurate concentration prediction is crucial for quality control.
• Near infra red (NIR) spectroscopy offers non-invasive analysis.

Purpose of the Study:

• To evaluate NIR spectroscopy for predicting concentrations of ibuprofen-nicotinamide (IBU-NIC) and carbamazepine-nicotinamide (CBZ-NIC) co-crystals.
• To develop and validate partial least squares (PLS) regression models for co-crystal quantification.
• To assess the feasibility of NIR spectroscopy as a Process Analytical Technology (PAT) tool.

Main Methods:

• Development of PLS regression models using calibration and validation datasets.
• Analysis of pharmaceutical co-crystal mixtures (IBU-NIC and CBZ-NIC) with NIR spectroscopy.
• Assessment of model accuracy using RMSEC, RMSEP, and correlation coefficients.

Main Results:

• Accurate PLS regression models were successfully developed for both IBU-NIC and CBZ-NIC co-crystal quantification.
• The IBU-NIC model demonstrated lower prediction errors compared to the CBZ-NIC model.
• NIR spectroscopy proved effective in predicting co-crystal concentration in powder mixtures with APIs.

Conclusions:

• NIR spectroscopy, coupled with PLS regression, is a viable tool for predicting pharmaceutical co-crystal concentrations.
• The developed models can be applied to monitor co-crystal content during manufacturing.
• This study supports the use of NIR as a PAT tool in pharmaceutical co-crystal production.