A Novel Method for Rapid Particle Size Analysis of Ibuprofen Using Near-infrared Spectroscopy

Development of novel portable NIR spectroscopy process analytical technology (PAT) tool for monitoring the transition of ibuprofen to ibuprofen sodium during wet granulation process

The aim of this research was to develop a process analytical technology (PAT) tool for monitoring the transformation of the active ingredient ibuprofen into the fast-dissolving salt ibuprofen sodium during the wet granulation process. Two near-infrared (NIR) spectrophotometers, portable and benchtop spectrophotometer, were compared. During the analysis with the built models, both demonstrated comparable accuracy and precision (R2X = 0.995, R2Y = 0.927, Q2 = 0.995, and R2X = 0.990, R2Y = 0.948, Q2 = 0.992, respectively). Considering the applicability, a model based on the portable NIR spectroscopic data was chosen for further development and application as a PAT tool for monitoring different steps during the wet granulation process. The evaluation of the model's predictive capability involved analyzing laboratory trial batches with varying amounts of sodium carbonate, resulting in different concentrations of ibuprofen sodium at the end of the wet granulation process. Subsequently, tablets were manufactured from each trial batch, followed by dissolution analysis. The dissolution rate assays were in good agreement with the NIR-predicted concentrations of ibuprofen sodium at the end of the wet granulation process. Based on the results, the proposed model provides an excellent tool to monitor the ibuprofen acid-salt transformation, to determine the end-point of the reaction, and to efficiently control the wet granulation process.

Application Research of Visible Near-Infrared Spectroscopy Technology for Detecting Intracerebral Hematoma

OBJECTIVE

To explore the visible near-infrared spectroscopic (VNIRS) characteristics of intracerebral hematoma, and provide experimental basis for hematoma localization and residual detection in hypertensive intracerebral hemorrhage (HICH) surgery.

METHODS

Using VNIRS, spectral data of cerebral hematoma and cortex were collected during HICH craniotomy, and characteristic spectra were matched with paired-sample T-test. A partial least squares (PLS) quantitative model for cerebral hematoma spectra was established.

RESULTS

The reflectance of cerebral hematoma spectra in the 500-800 nm band was lower than that of the cortex, and there were statistically significant differences in the 510, 565, and 630 nm bands (P < 0.05). The calibration correlation coefficient of the PLS quantitative model for cerebral hematoma spectra was R2 = 0.988, the cross-validation correlation coefficient was R2cv = 0.982, the root mean square error of calibration was RMSEC = 0.101, the root mean square error of cross-validation was RMSEV = 0.122, the external validation correlation coefficient was CORRELATION = 0.902, and the root mean square error of prediction was RMSEP = 0.426, indicating that the model had high fitting degree and good predictive ability.

CONCLUSIONS

VNIRS as a noninvasive, real-time and portable analysis technology, can be used for real-time detection of hematoma during HICH surgery, and provide reliable basis for hematoma localization and residual detection.

The Use of Near-infrared as Process Analytical Technology (PAT) during 3D Printing Tablets at the Point-of-Care

Fused deposition modelling (FDM) is one of the most researched 3D printing technologies that holds great potential for low-cost manufacturing of personalised medicine. To achieve real-time release, timely quality control is a major challenge for applying 3D printing technologies as a point-of-care (PoC) manufacturing approach. This work proposes the use of a low-cost and compact near-infrared (NIR) spectroscopy modality as a process analytical technology (PAT) to monitor a critical quality attribute (drug content) during and after FDM 3D printing process. 3D printed caffeine tablets were used to manifest the feasibility of the NIR model as a quantitative analytical procedure and dose verification method. Caffeine tablets (0-40% w/w) were fabricated using polyvinyl alcohol and FDM 3D printing. The predictive performance of the NIR model was demonstrated in linearity (correlation coefficient, R²) and accuracy (root mean square error of prediction, RMSEP). The actual drug content values were determined using the reference high-performance liquid chromatography (HPLC) method. The model of full-completion caffeine tablets demonstrated linearity (R² = 0.985) and accuracy (RMSEP =1.4%), indicated to be an alternative dose quantitation method for 3D printed products. The ability of the models to assess caffeine contents during the 3D printing process could not be accurately achieved using the model built with complete tablets. Instead, by building a predictive model for each completion stage of 20%, 40%, 60% and 80%, the model of different completion caffeine tablets displayed linearity (R² of 0.991, 0.99, 0.987, and 0.983) and accuracy (RMSEP of 2.22%, 1.65%, 1.41%, 0.83%), respectively. Overall, this study demonstrated the feasibility of a low NIR model as a non-destructive, low-cost, compact, and rapid analysis dose verification method enabling the real-time release to facilitate 3D printing medicine production in the clinic.