Supervised Extended Iterative Optimization Technology for Estimation of Powder Compositions in Pharmaceutical Applications: Method and Lifecycle Management

Continuous manufacturing of pharmaceutical products: A density-insensitive near infrared method for the in-line monitoring of continuous powder streams

Near infrared (NIR) spectroscopy is a valuable analytical technique for monitoring chemical composition of powder blends in continuous pharmaceutical processes. However, the variation in density captured by NIR during spectral collection of dynamic powder streams at different flow rates often reduces the performance and robustness of NIR models. To overcome this challenge, quantitative NIR measurements are commonly collected across all potential manufacturing conditions, including multiple flow rates to account for the physical variations. The utility of this approach is limited by the considerable quantity of resources required to run and analyze an extensive calibration design at variable flow rates in a continuous manufacturing (CM) process. It is hypothesized that the primary variation introduced to NIR spectra from changing flow rates is a change in the density of the powder from which NIR spectra are collected. In this work, powder stream density was used as an efficient surrogate for flow rate in developing a quantitative NIR method with enhanced robustness against process rate variation. A density design space of two process parameters was generated to determine the conditions required to encompass the apparent density and spectral variance from increases in process rate. This apparent density variance was included in calibration at a constant low flow rate to enable the development of a density-insensitive NIR quantitative model with limited consumption of materials. The density-insensitive NIR model demonstrated comparable prediction performance and flow rate robustness to a traditional NIR model including flow rate variation ("gold standard" model) when applied to monitoring drug content in continuous runs at varying flow rates. The proposed platform for the development of in-line density-insensitive NIR methods is expected to facilitate robust analytical model performance across variable continuous manufacturing production scales while improving the material efficiency over traditional robust modeling approaches for calibration development.

The Use of a Closed Feed Frame for the Development of Near-Infrared Spectroscopic Calibration Model to Determine Drug Concentration

PURPOSE

This study evaluates the use of the closed feed frame as a material sparing approach to develop near-infrared (NIR) spectroscopic calibration models for monitoring blend uniformity. The effect of shear induced by recirculation on NIR spectra was also studied.

METHODS

Calibration models were developed using NIR spectra obtained in the closed feed frame for two cases. For case 2, blends that flowed through the open feed frame were predicted with the model. The shear effect of the feed frame on the blends was assessed through the characterization of powder properties before and after recirculation.

RESULTS

The physical characterization of the blends confirmed that the powder properties were not altered after recirculation within the closed feed frame. Both calibration models provided highly accurate predictions of the test sets with low bias (0.03% w/w and -0.06% w/w) and relative standard error of prediction (1.9% and 3.7%), respectively. The predictive performance of the calibration models was not affected by the shear effect.

CONCLUSION

Recirculation within the closed feed frame did not change the physical properties of the blends studied. The prediction of blends flowing through the open feed frame was possible with a calibration model developed in the closed feed frame. The closed feed frame could reduce the materials needed to develop calibration models by more than 90%.

An Optimization Approach Coupling Preprocessing with Model Regression for Enhanced Chemometrics

Chemometric methods are broadly used in the chemical and biochemical sectors. Typically, derivation of a regression model follows data preprocessing in a sequential manner. Yet, preprocessing can significantly influence the regression model and eventually its predictive ability. In this work, we investigate the coupling of preprocessing and model parameter estimation by incorporating them simultaneously in an optimization step. Common model selection techniques rely almost exclusively on the performance of some accuracy metric, yet having a quantitative metric for model robustness can prolong model up-time. Our approach is applied to optimize for model accuracy and robustness. This requires the introduction of a novel mathematical definition for robustness. We test our method in a simulated set up and with industrial case studies from multivariate calibration. The results highlight the importance of both accuracy and robustness properties and illustrate the potential of the proposed optimization approach toward automating the generation of efficient chemometric models.

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.

Comparison Between Pure Component Modeling Approaches for Monitoring Pharmaceutical Powder Blends with Near-Infrared Spectroscopy in Continuous Manufacturing Schemes

Near-infrared (NIR) spectroscopy has become an important process analytical technology (PAT) for monitoring and implementing control in continuous manufacturing (CM) schemes. However, NIR requires complex multivariate models to properly extract the relevant information and the traditional model of choice, partial least squares, can be unfavorable on account of its high material and time investments for generating calibrations. To account for this, pure component-based approaches have been gaining attention due to their higher flexibility and ease of development. In the present study, the application of two pure component approaches, classical least squares (CLS) models and iterative optimization technology (IOT) algorithms, to pharmaceutical powder blends in a continuous feed frame was considered. The approaches were compared from both a model performance and practical implementation perspective. IOT were found to demonstrate superior performance in predicting drug content compared to CLS. The practical implementation of each modelling approach was also given consideration.

Method development and validation of a near-infrared spectroscopic method for in-line API quantification during fluidized bed granulation

Near-infrared spectroscopy (NIRS) is an excellent process analytical technology (PAT) tool for active pharmaceutical ingredient (API) quantification during fluidized granulation. Therefore, a portable near-infrared spectrometer combined with a new innovative method of extended iterative optimization technique (EIOT) was used to in-line monitor the API content uniformity during fluidized bed granulation. The principal component analysis (PCA) and partial least squares regression (PLSR) were also used to characterize and predict API concentration with changes from 75% to 125% of the label claim to prove the superiority of EIOT. The API content prediction accuracy of the EIOT method was verified through offline High Performance Liquid Chromatography (HPLC) measurement. Also, the spatial distribution of API in granules was visualized by Raman imaging technology. The results showed that the established NIRS method was suitable for the prediction of API content in fluidized bed granulation, which provides a new idea for the determination of API content during granulation.

A hybrid NIR-soft sensor method for real time in-process control during continuous direct compression manufacturing operations

Near Infrared (NIR) spectroscopy is commonly utilized for continuous manufacturing as Process Analytical Technology (PAT) tool. This paper focus on a continuous direct compression manufacturing process, in which an NIR PAT probe is integrated into the tablet press feed frame and into the tablet diversion control system to ensure continuous monitoring of the potency and homogeneity of the blend within the process line. The quantification of NIR spectra is achieved through Partial Least-Squares (PLS) modeling, calibrated with offline analyzed tablet cores at different potency levels. Because the NIR measurements are often sensitive to sample physical properties caused by raw materials or process conditions, etc., adopting a data-driven approach will require a large amount of representative data throughout the method lifecycle. During the early stages of process development, whenever new uncaptured source of variability in the model space are encountered, the chemometric predictions can deviate from the offline reference, requiring frequent model updates. These deviations can be reduced by integrating process and physico-chemical knowledge in the on-line potency estimation. This paper presents a novel hybrid method combining the online NIR PLS and a potency soft sensor estimation, enabling a robust potency prediction whilst minimizing maintenance downtimes and facilitating cross-site method transfer.

Digital Twins in Pharmaceutical and Biopharmaceutical Manufacturing: A Literature Review

The development and application of emerging technologies of Industry 4.0 enable the realization of digital twins (DT), which facilitates the transformation of the manufacturing sector to a more agile and intelligent one. DTs are virtual constructs of physical systems that mirror the behavior and dynamics of such physical systems. A fully developed DT consists of physical components, virtual components, and information communications between the two. Integrated DTs are being applied in various processes and product industries. Although the pharmaceutical industry has evolved recently to adopt Quality-by-Design (QbD) initiatives and is undergoing a paradigm shift of digitalization to embrace Industry 4.0, there has not been a full DT application in pharmaceutical manufacturing. Therefore, there is a critical need to examine the progress of the pharmaceutical industry towards implementing DT solutions. The aim of this narrative literature review is to give an overview of the current status of DT development and its application in pharmaceutical and biopharmaceutical manufacturing. State-of-the-art Process Analytical Technology (PAT) developments, process modeling approaches, and data integration studies are reviewed. Challenges and opportunities for future research in this field are also discussed.