Use of Raman spectroscopy and PLS for the quantification of critical quality attributes in biopharmaceutical products.

Science-based bioprocess design for filamentous fungi

The traditional drug manufacturing process involves numerous qualitative attributes that directly impact product quality. The Process Analytical Technology approach considers that to enhance process control, identifying critical process parameters and critical quality attributes that affect the manufacturing process is very much necessary. The Ayurvedic drug manufacturing process is more driven by fuzzy qualitative attributes. The present study was executed to identify the Critical Process Parameters and Critical Quality Attributes in the manufacturing process of a herbo-mineral formulation, viz. Arogyavardhini Rasa. Fuzzy set Qualitative Comparative Analysis (fsQCA) methodology was adopted to observe and identify the critical parameters. The study was executed in three steps, viz. Data collection, Data arrangement, and Analysis. The raw data collected was arranged and analyzed in the software R studio using the package QCA in four steps, viz. Calibration, Analysis of Necessity and Sufficiency, Truth Table construction, and Minimization. The results obtained show that Size reduction, Mardana, and Drying are the identified Critical Process Parameters that, in combination, lead to the outcome, i.e., good product quality. Thus, this study proves that Fuzzy Set Qualitative Comparative Analysis can be used as an efficient tool for the identification and measurement of the Critical Process Parameters that affect the Critical Quality Attributes and, thereby, the product quality in the manufacturing of Arogyavardhini Rasa.

Biotherapeutics, such as those derived from monoclonal antibodies (mAbs), are industrially produced in controlled multiunit operation bioprocesses. Each unit operation contributes to the final characteristics of the bioproduct. The complexity of the bioprocesses, the cellular machinery, and the bioproduct molecules, typically leads to inherent heterogeneity and variability of the final critical quality attributes (CQAs). In order to improve process control and increase product quality assurance, online and real-time monitoring of product CQAs is most relevant. In this review, the recent advances in CQAs monitoring of biotherapeutic drugs, with emphasis on mAbs, and throughout, the different bioprocess unit operations are reviewed. Recent analytical techniques used for assessment of product-related CQAs of mAbs are considered in light of the analytical speed and ability to measure different CQAs. Furthermore, the state of art modeling approaches for CQA estimation in real-time are presented as a viable alternative for real-time bioproduct CQA monitoring under the process analytical technology and quality-by-design frameworks in the biopharmaceutical industry, which have recently been demonstrated.

Applications of process analytical technology to crystallization processes

According to FDA guidance, PAT (Process Analytical Technology) is a system for designing, analyzing, and controlling manufacturing through timely measurements (i. e., during processing) of critical quality and preformance attributes of raw and in-process materials and processes. A desired goal of the PAT framework is to design and develop well understood processes that will consistently ensure a predefined quality at the end of the manufacturing process. PAT tools can be categorized according to the following: 1) Multivariate tools for design, data acquisition and analysis, 2) Process analyzers, 3) Process control tools, 4) Continuous improvement and knowledge management tools.In this article, process analyzers are introduced and discussed with their actual application under the recognition of PAT as a system.

Prediction of entire tablet formulations from pure powder components’ spectra via a two-step non-linear optimization methodology

Real-time determination of critical quality attributes using near-infrared spectroscopy: A contribution for Process Analytical Technology (PAT)

The purpose of the study was to develop a model to predict the critical quality attribute (CQA) of tablets during continuous and batch manufacturing using only critical material attributes (CMAs). Experiments were performed using ethenzamide as the active pharmaceutical ingredient processed with batch and continuous high-shear granulators. The disintegration time of tablets was defined as the CQA, and the particle-size distribution of granules and tablet hardness were defined as the CMAs. We first investigated the influence of granulation conditions on particle-size distribution during batch and continuous granulation. We then proceeded to construct the CQA estimation model by producing tables using batch and continuous granulation. The results indicated the similarity of the granulation mechanisms, as observed by the bimodality of the distributions and the significant causal factors. Principal component analysis revealed that the CQA was influenced strongly by the particle-size distribution and that the CMA–CQA correlations were similar for both processes. Finally, a model based on partial least-squares regression could be developed that could reasonably estimate the CQA using CMAs without involving any process parameters. This approach of using process-independent CQA prediction could enable flexible switching between batch and continuous manufacturing during a product life cycle, thus offering new possibilities for efficient life cycle management.

Real-time monitoring of integrated continuous granulation/drying line system “LaVortex®” for the pharmaceutical manufacturing of acetaminophen oral dosage formulations using near-infrared spectroscopy

A robust manufacturing process and the relationship between intermediate quality attributes (IQAs), critical quality attributes (CQAs), and critical process parameters (CPPs) for high-shear wet granulation was determined in this study. Based on quality by the design (QbD) approach, IQAs, CQAs, and CPPs of a telmisartan tablet prepared by high-shear wet granulation were determined and then analyzed with multivariate analysis (MVA) to evaluate mutual interactions between IQAs, CQAs, and CPPs. The effects of the CPPs on the IQAs and CQAs were quantitatively predicted with empirical models of best fit. The models were used to define operating space, and an evaluation of the risk of uncertainty in model prediction was performed using Monte Carlo simulation. MVA showed that granule size and granule hardness were significantly related to % dissolution. In addition, granule FE (Flow Energy) and Carr’s index had effects on tablet tensile strength. Using the manufacture of a clinical batch and robustness testing, a scale-up from lab to pilot scale was performed using geometric similarity, agitator torque profile, and agitator tip speed. The absolute biases and relative bias percentages of the IQAs and CQAs generated by the lab and pilot scale process exhibited small differences. Therefore, the results suggest that a risk reduction in the manufacturing process can be obtained with integrated process parameters as a result of the QbD approach, and the relationship between IQAs, CQAs, and CPPs can be used to predict CQAs for a control strategy and SUPAC (Scale-Up and Post-Approval Guidance).

VasLine: Realize online detection and augmented NIR using deep learning

Comparison of chemometrics strategies for the spectroscopic monitoring of active pharmaceutical ingredients in chemical reactions

Creation of novel large dataset comprising several granulation methods and the prediction of tablet properties from critical material attributes and critical process parameters using regularized linear regression models including interaction terms.

Downstream sample purification for quality attribute analysis is a significant bottleneck in process development for non-antibody biologics. Multi-step chromatography process train purifications are typically required prior to many critical analytical tests. This prerequisite leads to limited throughput, long lead times to obtain purified product, and significant resource requirements. In this work, immunoaffinity purification technology has been leveraged to achieve single-step affinity purification of two different enzyme biotherapeutics (Fabrazyme® [agalsidase beta] and Enzyme 2) with polyclonal and monoclonal antibodies, respectively, as ligands. Target molecules were rapidly isolated from cell culture harvest in sufficient purity to enable analysis of critical quality attributes (CQAs). Most importantly, this is the first study that demonstrates the application of predictive analytics techniques to predict critical quality attributes of a commercial biologic. The data obtained using the affinity columns were used to generate appropriate models to predict quality attributes that would be obtained after traditional multi-step purification trains. These models empower process development decision-making with drug substance-equivalent product quality information without generation of actual drug substance. Optimization was performed to ensure maximum target recovery and minimal target protein degradation. The methodologies developed for Fabrazyme were successfully reapplied for Enzyme 2, indicating platform opportunities. The impact of the technology is significant, including reductions in time and personnel requirements, rapid product purification, and substantially increased throughput. Applications are discussed, including upstream and downstream process development support to achieve the principles of Quality by Design (QbD) as well as integration with bioprocesses as a process analytical technology (PAT).

Objective To establish and validate a framework for the transfer of a sinomenine hydrochloride sustained-release tablet from a legacy batch process to a pilot-scale continuous direct compression (CDC) manufacturing line, without reformulation. Significance Transferring existing products to continuous manufacturing (CM) without reformulation is challenging. In this work, a significant contribution is made through the validation of a data-driven pathway aligned with QbD and ICH Q13 to de-risk this transition. Methods A pilot-scale CDC line, operating at 1.9 kg/h, was utilized, comprising loss-in-weight feeders, a continuous blender, and a rotary tablet press. A process analytical technology (PAT)-enabled control strategy was implemented, featuring a robust partial least squares (PLS) model developed for real-time API quantification via in-line near-infrared (NIR) spectroscopy. Process dynamics were characterized using residence time distribution (RTD) analysis. Results Robust stability and control were demonstrated by the CDC process during a 300-minute steady-state run. Through real-time monitoring, API content uniformity was confirmed to be consistently maintained within ±3%. Critically, tablets produced via CDC were proven to be equivalent to the reference batch product across all critical quality attributes, including physical properties and functionally identical 24-hour dissolution profiles. Conclusion In this study, a comprehensive framework for the batch-to-continuous transfer of a sustained-release tablet was successfully validated. Process robustness was ensured by the PAT-enabled control strategy. Theapproach is confirmed as a low-risk, guideline-compliant pathway for modernizing the manufacturing of existing pharmaceutical products.