Design of Heterogeneity Indices for Blending Quality Assessment Based on Hyperspectral Images and Variographic Analysis

Forensic Discrimination of Drug Powder Based on Drug Mixing Condition Determined Using Micro Fourier Transform Infrared Spectroscopy

The quantitative evaluation of the drug mixing condition was conducted for application in the forensic discrimination of drug powders using micro Fourier transform infrared (FT-IR) spectroscopy. Bromhexine hydrochloride (BHCl) and p-hydroxybenzoic acid (PHBA) were used as the simulated drug and additive, respectively. Equal masses of two chemicals were (1) simply mixed, (2) homogenized using agate mortar, or (3) dissolved in methanol and dried, and then (4) homogenized using agate mortar. The mixed powders dispersed on BaF₂ plates were subjected to mapping analysis of micro FT-IR spectroscopy using synchrotron radiation (SR) or globar light in transmission mode with aperture sizes of 2.5 x 2.5 and 10 x 10μm², and x-y scanning steps of 2.5 and 10 μm, respectively. The areas of the vibration bands specific to BHCl (C-N bending) and PHBA (C=O stretching) were converted to the molar contents (C_BHCl, C_PHBA), and the relative content ratio (RCR: C_PHBA/[C_BHCl + C_PHBA]) was used as one mixing parameter. The resulting two-dimensional distribution map provided the relative spatial localizations of the two species, and frequency histograms with a horizontal axis of RCR were plotted to evaluate the RCR distribution. The percentage frequency of the extreme value in which RCR was 0 or 1 (%EV) was used as one mixing index. After excluding the extreme values, the coefficient of variation (CV) of the RCR distribution was used as another mixing index. The differentiation among four mixing modes could be evaluated from the standpoint of %EV and CV, and the discrimination capacity by SR instrument was superior to that by globe light instrument.

Development of a Near-Infrared Spectroscopy (NIRS)-Based Characterization Approach for Inherent Powder Blend Heterogeneity in Direct Compression Formulations

With the advent of continuous direct compression (CDC) process, it becomes increasingly desirable to characterize inherent powder blend heterogeneity at a small batch scale for a robust and CDC-amenable formulation. To accomplish this goal, a near infrared spectroscopy (NIRS)-based characterization approach was developed and implemented on multiple direct compression (DC) blends in this study, with the intended purpose of complementing existing formulation development tools and enabling to build an early CMC data package for late-phased process analytical technology (PAT) method development. Three fumaric acid DC blends, designed to harbor varied degrees of inherent blend heterogeneity, were employed. Near infrared spectral data were collected on a kg-scale batch blender via both time- and angle-based triggering modes. The time-triggered data were used to investigate the blending heterogeneity with respect to rotation angles, while the angle-triggered data were used to provide blending variability characterization and compare against off-line HPLC-based results. The time-triggered data revealed that the greatest blend variability was observed between revolutions, while the blending variability within a single revolution stayed relatively low with respect to rotation angles. This confirmed earlier literature findings that the bottom layer of powder blends tends to move with the blender within each revolution, and the most intense powder mixing takes place across revolutions. This also indicates the use of blending speed and the number of co-adds are not able to increase sampling volume to improve signal-to-noise ratio under a tumble-bin blender as what were typically done in a feedframe application. The angle-triggered data showed that there is a consistent trend between NIRS and HPLC-based methods on characterizing blend heterogeneity across the blends at a given sample size. This study contributes to establishing NIRS as a potential characterization approach for inherent powder blend heterogeneity for early R&D. It also highlights the promise of continuous characterization of inherent powder blend heterogeneity from gram scale to mini-batch CDC scale.

Neither too little nor too much: Finding the ideal proportion of excipients using confocal Raman and chemometrics

The applications of Raman imaging in pharmaceutical field are ever-increasing due its ability to obtain spatial and spectral information simultaneously, once it allows determine the chemical distribution of compounds. In this sense, it is used to study homogeneity, of paramount importance during the development of pharmaceutical formulations due to its relation to stability, safety and efficacy. Commonly, just surface is analyzed, but confocal Raman spectroscopy can also characterize the inner part of samples, allowing to determine phase separation in the early stages. In this sense, confocal 3D Raman microscopy was crucial to obtain the optimal proportion of Apifil®, Capryol® 90 and Transcutol® to promote controlled release of the local anesthetic butamben (BTB). 3D chemical maps were obtained by classical least squares (CLS) using pure compound spectra as S matrix, showing that chemical distribution throughout the material was different. Knowing that the composition of samples affects the homogeneity parameter, standard deviation and distributional homogeneity index (DHI) were used in mixture experimental design (DoE). From this analysis, it was revealed that a correct amount of Capryol® 90 enhances both miscibility and solubility. Furthermore, suitable miscibility was observed in two ratio proportions of excipients with a desirability of 0.783 and 0.742. These results unequivocally demonstrated that confocal Raman microscopy combined to DoE can bring pharmaceutical development to a higher level.

A Review of Pharmaceutical Robot based on Hyperspectral Technology

The quality and safety of medicinal products are related to patients’ lives and health. Therefore, quality inspection takes a key role in the pharmaceutical industry. Most of the previous solutions are based on machine vision, however, their performance is limited by the RGB sensor. The pharmaceutical visual inspection robot combined with hyperspectral imaging technology is becoming a new trend in the high-end medical quality inspection process since the hyperspectral data can provide spectral information with spatial knowledge. Yet, there is no comprehensive review about hyperspectral imaging-based medicinal products inspection. This paper focuses on the pivotal pharmaceutical applications, including counterfeit drugs detection, active component analysis of tables, and quality testing of herbal medicines and other medical materials. We discuss the technology and hardware of Raman spectroscopy and hyperspectral imaging, firstly. Furthermore, we review these technologies in pharmaceutical scenarios. Finally, the development tendency and prospect of hyperspectral imaging technology-based robots in the field of pharmaceutical quality inspection is summarized.

SWiVIA - Sliding window variographic image analysis for real-time assessment of heterogeneity indices in blending processes monitored with hyperspectral imaging

Controlling blending processes of solid material using advanced real-time sensing technologies tools is crucial to guarantee the quality attributes of manufactured products from diverse industries. The use of process analytical technology (PAT) tools based on chemical imaging systems are useful to assess heterogeneity information during mixing processes. Recently, a powerful procedure for heterogeneity assessment based on the combination of off-line acquired chemical images and variographic analysis has been proposed to provide specific heterogeneity indices related to global and distributional heterogeneity. This work proposes a novel PAT tool combining in situ chemical imaging and variogram-derived quantitative heterogeneity indices for the real-time monitoring of blending processes. The proposed method, so called sliding window variographic image analysis (SWiVIA), derives heterogeneity indices in real-time associated with a sliding image window that moves continuously until the full blending time interval is covered. The SWiVIA method is thoroughly assessed paying attention at the effect of relevant factors for continuous blending monitoring and heterogeneity description, such as the scale of scrutiny needed for heterogeneity definition or the blending period defined to set the sliding image window. SWiVIA is tested on blending runs of pharmaceutical and food products monitored with an in situ near-infrared chemical imaging system. The results obtained help to detect abnormal mixing phenomena and can be the basis to establish blending process control indicators in the future. SWiVIA is adapted to study blending behaviors of the bulk product or compound-specific blending evolutions.