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.

Benchtop NIR method development for continuous manufacturing scale to enable efficient PAT application for solid oral dosage form.

A Near Infrared (NIR) method was developed using a small benchtop feed frame system to quantify Saccharin potency in a powder blend during continuous manufacturing process. A 15-point Design of Experiments (DoE) was created based on the NIR spectral response and compositions of the formulation to develop a calibration set. The calibration set was designed to create compositional and raw material lots variation using minimum resources. The calibration experiments utilized around 0.5 kg Saccharin (Active Pharmaceutical Ingredient (API) surrogate) and 1.8 kg of excipients. Partial Least Square (PLS) modeling was used to develop a quantitative NIR method from the calibration data. The NIR method was implemented during 5 test batches in two different manufacturing sites across different potency levels at a continuous manufacturing platform for direction compression. Acceptable prediction performance was achieved from the NIR method at both sites. The NIR method was robust against changes in process scale and NIR instruments. The variance information built into the calibration set was found to be critical to successful model performance. This study shows a benchtop feed frame can be used for material sparing calibration method development without operating at a full-scale process line and applied across multiple sites, instruments at different potency levels.

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.

Multivariate Spectroscopic Method Lifecycle Management as Part of the Quality Management System.

Multivariate model based spectroscopic methods require model maintenance through their lifecycle. A survey conducted by the International Consortium for Innovation and Quality in Pharmaceutical Development (IQ) in 2019 showed that regulatory reporting categories for the model related changes can be a hurdle for the routine use of these types of methods. This article introduces industry best practices on multivariate method and model lifecycle management within the Pharmaceutical Quality System. Case studies are provided to demonstrate how the Established Conditions and Post-Approval Change Management Protocol concepts may be leveraged to allow regulatory flexibility for change management and to encourage the use of these techniques for the development and commercialization of pharmaceutical products.

An agile and robust in-line NIR potency deviation detection method for monitoring and control of a continuous direct compression process.

Near Infrared (NIR) method for blend potency estimation has been commonly used as an essential tool for process monitoring and control in continuous manufacturing of solid oral dosage forms. Robustness has been the main challenge for successful application of an NIR method, which often results in a long development time with frequent method update. Robustness deficiency often presents as an offset (bias) on the mean potency estimation. In this paper, the purpose of the NIR method has been redefined from estimating potency to potency deviation. This quantitative approach uses the mean centered potency to estimate potency deviations from the process mean, therefore, detects the non-conforming materials for continuous process monitoring and control. An NIR method was developed at the lab benchtop scale and directly deployed to a direct compression continuous manufacturing platform at Pfizer for mean centered potency estimation. The benchtop calibration provided a speedy and efficient NIR method development and the method showed enhanced robustness for estimating potency deviation in presence of wide process and raw material variations. Integrating with the mean centered approach, the NIR model from the lab could be implemented to different sites using different instruments without requiring model update for the established range of process conditions and raw material properties.

First-Principles and Empirical Approaches to Predicting In Vitro Dissolution for Pharmaceutical Formulation and Process Development and for Product Release Testing.

This manuscript represents the perspective of the Dissolution Working Group of the International Consortium for Innovation and Quality in Pharmaceutical Development (IQ) and of two focus groups of the American Association of Pharmaceutical Scientists (AAPS): Process Analytical Technology (PAT) and In Vitro Release and Dissolution Testing (IVRDT). The intent of this manuscript is to show recent progress in the field of in vitro predictive dissolution modeling and to provide recommended general approaches to developing in vitro predictive dissolution models for both early- and late-stage formulation/process development and batch release. Different modeling approaches should be used at different stages of drug development based on product and process understanding available at those stages. Two industry case studies of current approaches used for modeling tablet dissolution are presented. These include examples of predictive model use for product development within the space explored during formulation and process optimization, as well as of dissolution models as surrogate tests in a regulatory filing. A review of an industry example of developing a dissolution model for real-time release testing (RTRt) and of academic case studies of enabling dissolution RTRt by near-infrared spectroscopy (NIRS) is also provided. These demonstrate multiple approaches for developing data-rich empirical models in the context of science- and risk-based process development to predict in vitro dissolution. Recommendations of modeling best practices are made, focused primarily on immediate-release (IR) oral delivery products for new drug applications. A general roadmap is presented for implementation of dissolution modeling for enhanced product understanding, robust control strategy, batch release testing, and flexibility toward post-approval changes.

Designing a calibration set in spectral space for efficient development of an NIR method for tablet analysis.

Designing a calibration set is the first step in developing a multivariate spectroscopic calibration method for quantitative analysis of pharmaceutical tablets. This step is critical because successful model development depends on the suitability of the calibration data. For spectroscopic-based methods, traditional concentration based techniques for designing calibration sets are prone to have redundant information while simultaneously lacking necessary information for a successful calibration model. A method for designing a calibration set in spectral space was developed. The pure component spectra of a tablet formulation were used to define the spectral space of that formulation. This method maximizes the information content of measurements and minimizes sample requirements to provide an efficient means for developing multivariate spectroscopic calibration. A comparative study was conducted between a commonly employed full factorial approach to calibration development and the newly developed technique. The comparison was based on a system to quantify a model drug, acetaminophen, in pharmaceutical compacts using near infrared spectroscopy. A 2-factor full factorial design (acetaminophen with 5 levels and MCC:Lactose with 3 levels) was used for calibration development. Three replicates at each design point resulted in a total of 45 tablets for the calibration set. Using the newly developed spectral based method, 11 tablets were prepared for the calibration set. Partial least square (PLS) models were developed from respective calibration sets. Model performance was comprehensively assessed based on the ability to predict acetaminophen concentrations in multiple prediction sets. One prediction set contained similar information to calibration set while the other prediction sets contained different information from calibration set in order to assess the model accuracy and robustness. Similar prediction performance was achieved using the 11-tablet design (spectral space), compared to the 45-tablet full factorial approach. This work demonstrates that a calibration set designed in spectral space provided an efficient means of developing spectroscopic multivariate calibration.

In-line monitoring and optimization of powder flow in a simulated continuous process using transmission near infrared spectroscopy.

In-line monitoring of continuous powder flow is an integral part of the continuous manufacturing process of solid oral dosage forms in the pharmaceutical industry. Specifically, monitoring downstream from loss-in-weight (LIW) feeders and/or continuous mixers provides important data about the state of the process. Such measurements support control of the process and thereby enhance product quality. Near Infrared Spectroscopy (NIRS) is a potential PAT tool to monitor the homogeneity of a continuous powder flow stream in pharmaceutical manufacturing. However, the association of analytical results from NIR sampling of the powder stream and the homogeneity (content uniformity) of the resulting tablets provides several challenges; appropriate sampling strategies, adequately robust modeling techniques and poor sensitivities (for low dose APIs) are amongst them. Information from reflectance-based NIRS sampling is limited. The region of the powder bed that is interrogated is confined to the surface where the measurement is made. This potential bias in sampling may, in turn, limit the ability to predict the homogeneity of the finished dosage form. Further, changes to the processing parameters (e.g., rate of powder flow) often have a significant effect on the resulting data. Sample representation, interdependence between process parameters and their effects on powder flow behavior are critical factors for NIRS monitoring of continuous powder flow system. A transmission NIR method was developed as an alternative technique to monitor continuous powder flow and quantify API in the powder stream. Transmission NIRS was used to determine the thickness of the powder stream flowing from a loss-in-weight feeder. The thickness measurement of the powder stream provided an in-depth understanding about the effects of process parameters such as tube angles and powder flow rates on powder flow behaviors. This knowledge based approach helped to define an analytical design space that was specific to flow properties and to determine the optimum process parameters for successful quantitation of powder stream. A PAT method based on transmission NIRS was developed to monitor the homogeneity of API in a continuously flowing powder stream.