Effect of excipient particle size distribution variability on compact tensile strength; and its in-line prediction by force-displacement and force-time profiling.

BACKGROUND: Direct compression is potentially sensitive to particle size distribution (PSD) variability in pharmaceutical grade excipients. Yet, the impact is insufficiently studied. Furthermore, the use of force sensor as a process analytical technology (PAT) platform, to monitor the effect of PSD variations on compact tensile strength, is a readily available but underutilized strategy. METHODS: To address these shortfalls, the effect of PSD variability on compaction was investigated. Low (4% w/w drug) and high (15% w/w drug) dose blends comprising chlorpheniramine, microcrystalline cellulose and spray-agglomerated lactose were tableted. The PSD of spray-agglomerated lactose was varied by adding ungranulated fines to simulate commercially-relevant variability. Tensile strength and disintegration time were determined. The use of force sensor, to generate force-displacement and force-time profiles, for in-line tensile strength prediction was evaluated. RESULTS: Increasing proportion of ungranulated fines (≥ 16%) reduced tensile strength by 10% and 4% in low and high dose formulations (p < 0.02). Increased friction during compaction hindered particle movement and reduced the energy available for bonding. Nevertheless, disintegration performances were equally acceptable for immediate drug release (≈ 30 s). Modelling of tensile strength with force-displacement and force-time profiles yielded ≥ 98% accuracy for in-line prediction (relative root mean square error of prediction = 3.7% and 4.8%). CONCLUSION: A better understanding of the relationship between PSD variability and direct compression was attained; and force-displacement and force-time profiling are pragmatic and elegant PAT strategies. Significantly, with further refinements, the force sensor in the rotary tablet press can be repurposed for process monitoring and quality inspection. This unlocks opportunities for process understanding and control, without additional investments in PAT platforms.

De-risking excipient particle size distribution variability with automated robust mixing: Integrating quality by design and process analytical technology.

BACKGROUND: Particle size distribution (PSD) variability in excipients affects mixing. In response, manufacturers rely on raw material control and rigidly defined process parameters to achieve quality. However, this status quo is costly; and diverges from regulatory exceptions for process robustness. Although robustness improves cost and material usage efficiency, it remains under-adopted. METHOD: To address this gap, a robust batch mixing operation that mitigated the impact of PSD variability was evaluated, with blends comprising chlorpheniramine, microcrystalline cellulose and lactose. PSD of lactose was varied to simulate commercially-relevant variability. Due to PSD-induced rheological variations, the blends had different optimal mixing speeds. For the automation study, near infrared (NIR) spectroscopy; process optimization and endpoint detection algorithms; and control hardware were integrated within a cluster of software environments. NIR spectroscopy was employed for in-line PSD characterization and blend monitoring, to modulate mixing speed and detect endpoint (feedforward and feedback control). RESULTS: NIR spectroscopy rapidly detected PSD variations by the 6th-9th rotations, to activate feedforward control, which mitigated the effect of PSD variability and reduced the mixing time by 13-34%. Endpoints were correctly detected. PSD variations and blend homogeneity were accurately predicted (relative standard error of prediction ≤ 2%). CONCLUSION: The automated robust mixing operation was successful. Pertinently, NIR spectrometer can be adopted for multimodal sensing. Its applicability for production-driven characterization of raw materials in batch and continuous pharmaceutical processing should be further explored. Lastly, this study laid the groundwork for end-to-end implementation of process analytical technology in robust batch processing.

3D projection and multivariate image analysis for quantitative visual modelling of mixability and rheological behavior of lactose powder.

BACKGROUND: This study reports the use of multivariate time and image analysis of avalanche videographic data for quantitative visual modelling of mixability. Its usefulness, in mechanistically modelling a powder's rheological behavior in relation to mixing, was evaluated. METHODS: Particle size distribution (PSD) of a pharmaceutical grade lactose powder was modified to reflect commercially encountered variability. The PSD variants were rheologically distinct and had different mixability. Avalanche testing was performed on the modified lactose powders. Avalanche rheological properties (ARP) profiles and videos were collected for numerical and quantitative visual modelling, respectively. In quantitative visual modelling, videos captured were transformed into serial projected images. Important features of the projected images were extracted as eigen-images, to derive the avalanche rheological visual metric (ARVM). Mixability was modelled as a function of ARP or ARVM and the rotation speed. RESULTS: Relative to the ARP model, the ARVM models were highly interpretable. As a univariate expression of ARP, ARVM also possessed construct validity (r2 greater than 0.99, slope ≥ 0.96). Important rheological features of the lactose powders were holistically visualized within a single eigen-image which enabled the generation of simpler models (5 versus 34 variables for ARP model). The ARVM models predicted mixability of lactose powders with greater accuracy than the ARP model (relative root mean square error of external validation ≤ 3.30% versus 4.96%). CONCLUSIONS: Quantitative visual modelling is a viable alternative to purely numerical approaches. Most significantly, the model's interpretability and concreteness enable manufacturers to readily understand the risk posed by PSD variability on manufacturing processes and swiftly take pre-emptive actions, without being mired in multivariate data complexity. In addition, the use of quantitative visual approach in time series imaging, for studying and monitoring industrial processes, could also be explored.

The effect of rotation speed and particle size distribution variability on mixability: An avalanche rheological and multivariate image analytical approach.

The utility of modulating rotation speed in tumble mixing and its mechanistic interplay with particle size distribution (PSD) variability in excipients remain underexplored. They were investigated in this study. For the present purpose, PSD of a commercial grade lactose was modified to reflect commercially relevant variations; and mixed with microcrystalline cellulose and chlorpheniramine in a double-cone blender, at various rotation speeds. Model of mixing was constructed using avalanche rheological properties and was also rendered as quantifiable visual models using avalanche rheological visual metric (ARVM), to uncover mechanistic relationships. ARVM was derived through multivariate image analysis of avalanche flow. It was observed that increasing rotation speed reduced the number of rotations needed to achieve blend homogeneity by 30-33% for PSD variants with 16-20% fines, while increasing the number of rotations by 134% in PSD variants with less than 15% fines (p ≈ 0.00). ARVM successfully modelled (root mean square error of external validation = 2.46%) and revealed the mechanistic interplay. With increased proportion of fines, lactose exhibited quasi-parabolic motion with disaggregation of soft agglomerates and improved mixing. With decreased proportion of fines, lactose flowed as coherent wave-like masses with imperceptible dispersive tendency and increased dilation, which impeded mixing. In conclusion, this study contributes to process understanding and ideas for designing robust mixing operations. It showcases the usefulness of a quantitative visual approach, exemplified by the ARVM, to evaluate material variability and uncover its mechanistic impact on processing.

Near infrared spectroscopy for rapid and in-line detection of particle size distribution variability in lactose during mixing.

Particle size distribution (PSD) variability in excipients may cause unacceptable prolongation of mixing time needed to achieve blend homogeneity. Therefore, it is vital to modulate mixing through real-time monitoring of PSD variability. Notwithstanding the criticality of PSD variability, real-time measurement of PSD during mixing is relatively unexplored; and this is the focus of the present study. The model excipient was commercial grade lactose with modified PSD that conformed to the manufacturer's specifications. It was mixed with microcrystalline cellulose and chlorpheniramine in a double-cone blender. High and low dose blends were prepared and near infrared spectroscopy (NIRS) was used to collect spectral data, during mixing, for chemometric modelling of PSD. Four modelling approaches based on partial least squares regression (PLSR) were applied. The models were highly interpretable and rapidly measured PSD near the beginning of mixing (5th to 6th rotation), with accuracy (relative standard error of prediction <5.0%, r2 ≈ 1.00, slope ≈ 1.00). Therefore, NIR chemometric modelling is a viable strategy to detect variability in PSD of excipients during blending and could enable real-time control of mixing. Most significantly, this strategy is potentially transferable to the monitoring and controlling of batch and continuous processes, where PSD is either a source of process variability or a critical quality attribute.

Investigating the effect and mechanism of particle size distribution variability on mixing using avalanche testing and multivariate modelling.

Particle size distribution (PSD) variability in excipients is widely thought to affect the mixing process and the achievement of blend homogeneity. Yet, few studies have addressed this issue by attempting to ascertain the relationship and elucidate its mechanism. To address this, the model material, lactose, was modified to reflect commercially relevant PSD variations and mixed with microcrystalline cellulose and chlorpheniramine in a double-cone blender. Multivariate modelling and avalanche testing were applied to elucidate the relationship and mechanism. PSD variability can cause significant change in mixing time, by 8 times and 3 times (p ≈ 0.00) for high and low dose drug formulations, respectively. Achievement of blend homogeneity depended on the dispersive mixing mechanism (r2 = 0.99). Dispersive mixing was adversely affected by powder cohesiveness and powder dilation, which increased as the proportion of fine particles in lactose powder increased. This study yielded three conclusions. Firstly, PSD variability in pharmaceutical grade excipients can cause unacceptable prolongation in mixing time. Secondly, the impact of PSD variability on continuous mixing and other batch mixing of various scales, requires investigation. Lastly, the current findings can contribute to the development of robust mixing operations in the form of offline pre-emptive measures and inline process control strategies.

Clinical validation and implications of dried blood spot sampling of carbamazepine, valproic acid and phenytoin in patients with epilepsy.

To facilitate therapeutic monitoring of antiepileptic drugs (AEDs) by healthcare professionals for patients with epilepsy (PWE), we applied a GC-MS assay to measure three AEDs: carbamazepine (CBZ), phenytoin (PHT) and valproic acid (VPA) levels concurrently in one dried blood spot (DBS), and validated the DBS-measured levels to their plasma levels. 169 PWE on either mono- or polytherapy of CBZ, PHT or/and VPA were included. One DBS, containing ∼15 µL of blood, was acquired for the simultaneous measurement of the drug levels using GC-MS. Simple Deming regressions were performed to correlate the DBS levels with the plasma levels determined by the conventional immunoturbimetric assay in clinical practice. Statistical analyses of the results were done using MedCalc Version 12.6.1.0 and SPSS 21. DBS concentrations (Cdbs) were well-correlated to the plasma concentrations (Cplasma): r=0.8381, 0.9305 and 0.8531 for CBZ, PHT and VPA respectively, The conversion formulas from Cdbs to plasma concentrations were [0.89×CdbsCBZ+1.00]µg/mL, [1.11×CdbsPHT-1.00]µg/mL and [0.92×CdbsVPA+12.48]µg/mL respectively. Inclusion of the red blood cells (RBC)/plasma partition ratio (K) and the individual hematocrit levels in the estimation of the theoretical Cplasma from Cdbs of PHT and VPA further improved the identity between the observed and the estimated theoretical Cplasma. Bland-Altman plots indicated that the theoretical and observed Cplasma of PHT and VPA agreed well, and >93.0% of concentrations was within 95% CI (±2SD); and similar agreement (1∶1) was also found between the observed Cdbs and Cplasma of CBZ. As the Cplasma of CBZ, PHT and VPA can be accurately estimated from their Cdbs, DBS can therefore be used for drug monitoring in PWE on any of these AEDs.