Modeling in the quality by design environment: Regulatory requirements and recommendations for design space and control strategy appointment

Data-Driven Modeling of the Spray Drying Process. Process Monitoring and Prediction of the Particle Size in Pharmaceutical Production

Spray drying is used in the pharmaceutical industry for particle engineering of amorphous solid dispersions (ASDs). The particle size of the spray-dried (SD) powders is one of their key attributes due to its impact on the downstream processes and the drug product's functional properties. Offline and inline laser diffraction methods can be used to estimate the product's particle size; however, the final release of these ASDs is based on offline instruments. This paper presents a novel data-driven modeling approach for predicting the particle size of SD products. The model-based characterization of the process and the product's particle size, as a critical quality attribute, follows the quality by design principles. The resulting model can be used for online process monitoring, reducing the risks of out-of-specifications products and supporting their real-time release. A Tucker3 model is trained to capture and factorize the deterministic variability of the process. Subsequently, a partial least-squares regression model is calibrated to model the impact that variability in the input material properties, the process parameters, and the spray nozzle have on the products' particle size. This strategy has been calibrated and validated using large scale production data for two intermediate drug products under high sparsity of particle size data. Despite the challenges, high accuracy was obtained in predicting the median particle size (dv50) for release. The 99% confidence interval results in an error of maximum 2.5 μm, which is less than 10% of the allowed range of variation.

Question-based review for pharmaceutical development: An enhanced quality approach

Over the last years, the pharmaceutical industry has faced real challenges regarding quality assurance. In this context, the establishment of more holistic approaches to the pharmaceutical development has been encouraged. The emergence of the Quality by Design (QbD) paradigm as systematic, scientific and risk-based methodology introduced a new concept of pharmaceutical quality. In essence, QbD can be interpreted as a strategy to maximize time and cost savings. An in-depth understanding of the formulation and manufacturing process is demanded to optimize the safety, efficacy and quality of a drug product at all stages of development. This innovative approach streamlines the pharmaceutical Research and Development (R&D) process, provides greater manufacturing flexibility and reduces regulatory burden. To assist in QbD implementation, International Conference on Harmonisation (ICH), U.S. Food and Drug Administration (FDA) and European Medicines Agency (EMA) organized and launched QbD principles in their guidance for industry, identifying key concepts and tools to design and develop a high-quality drug product. Despite the undeniable advantages of the QbD approach, and the widespread information on QbD regulatory expectations, its full implementation in the pharmaceutical field is still limited. The present review aims to establish a crosswise overview on the current application status of QbD within the framework of the ICH guidelines (ICH Q8(R2) - Q14 and ICH Q2(R2)). Moreover, it outlines the way information gathered from the QbD methodology is being harmonized in Marketing Authorization Applications (MAAs) for European market approval. This work also highlights the challenges that hinder the deployment of the QbD strategy as a standard practice.

Flowsheet modelling of a powder continuous feeder-mixer system

In this study, an integrated flowsheet model of the continuous feeder-mixer system was calibrated, simulated and compared against experimental data. The feeding process was first investigated using two major components (ibuprofen and microcrystalline cellulose (MCC)), in a formulation comprised of: 30 wt% of ibuprofen, 67.5 wt% MCC, 2 wt% of sodium starch glycolate and 0.5 wt% of magnesium stearate. The impact of a refill on feeder performance was experimentally evaluated for different operating conditions. Results showed that it had no influence on feeder performance. While simulations with the feeder model fairly reproduced the material behaviour observed in the feeder, unintended disturbances were underpredicted due to the model's low complexity. Experimentally, mixer's efficiency was assessed based on ibuprofen residence time distribution. Mean residence time pointed to a higher mixer's efficiency at lower flow rates. Blend homogeneity results showed that for the entire set of experiments, ibuprofen RSD <5%, irrespective of process variables. A feeder-mixer flowsheet model was calibrated, after regressing the axial model coefficients. The regression curves exhibited a R² above 0.96, whereas the RMSE varied from 1.58x10^-4 to 1.06x10^-3 s^-1 across all fitted curves. Simulations confirmed that flowsheet model captured the powder dynamics inside the mixer and qualitatively predicted the mixer's filtering ability against feeding composition fluctuations, as well as ibuprofen RSD in blend, in line with real experiments.

Digital twin of a continuous chromatography process for mAb purification: Design and model-based control

Model-based design of integrated continuous train coupled with online process analytical technology (PAT) tool can be a potent facilitator for monitoring and control of Critical Quality Attributes (CQAs) in real time. Charge variants are product related variants and are often regarded as CQAs as they may impact potency and efficacy of drug. Robust pooling decision is required for achieving uniform charge variant composition for mAbs as baseline separation between closely related variants is rarely achieved in process scale chromatography. In this study, we propose a digital twin of a continuous chromatography process, integrated with an online HPLC-PAT tool for delivering real time pooling decisions to achieve uniform charge variant composition. The integrated downstream process comprised continuous multicolumn capture protein A chromatography, viral inactivation in coiled flow inverter reactor (CFIR), and multicolumn CEX polishing step. An online HPLC was connected to the harvest tank before protein A chromatography. Both empirical and mechanistic modeling have been considered. The model states were updated in real time using online HPLC charge variant data for prediction of the initial and final cut point for CEX eluate, according to which the process chromatography was directed to switch from collection to waste to achieve the desired charge variant composition in the CEX pool. Two case studies were carried out to demonstrate this control strategy. In the first case study, the continuous train was run for initially 14 h for harvest of fixed charge variant composition as feed. In the second case study, charge variant composition was dynamically changed by introducing forced perturbation to mimic the deviations that may be encountered during perfusion cell culture. The control strategy was successfully implemented for more than ±5% variability in the acidic variants of the feed with its composition in the range of acidic (13%-17%), main (18%-23%), and basic (59%-68%) variants. Both the case studies yielded CEX pool of uniform distribution of acidic, main and basic profiles in the range of 15 ± 0.8, 31 ± 0.3, and 53 ± 0.5%, respectively, in the case of empirical modeling and 15 ± 0.5, 31 ± 0.3, and 53 ± 0.3%, respectively, in the case of mechanistic modeling. In both cases, process yield for main species was >85% and the use of online HPLC early in the purification train helped in making quicker decision for pooling of CEX eluate. The results thus successfully demonstrate the technical feasibility of creating digital twins of bioprocess operations and their utility for process control.

The use of predictive models to develop chromatography-based purification processes

Chromatography is the workhorse of biopharmaceutical downstream processing because it can selectively enrich a target product while removing impurities from complex feed streams. This is achieved by exploiting differences in molecular properties, such as size, charge and hydrophobicity (alone or in different combinations). Accordingly, many parameters must be tested during process development in order to maximize product purity and recovery, including resin and ligand types, conductivity, pH, gradient profiles, and the sequence of separation operations. The number of possible experimental conditions quickly becomes unmanageable. Although the range of suitable conditions can be narrowed based on experience, the time and cost of the work remain high even when using high-throughput laboratory automation. In contrast, chromatography modeling using inexpensive, parallelized computer hardware can provide expert knowledge, predicting conditions that achieve high purity and efficient recovery. The prediction of suitable conditions in silico reduces the number of empirical tests required and provides in-depth process understanding, which is recommended by regulatory authorities. In this article, we discuss the benefits and specific challenges of chromatography modeling. We describe the experimental characterization of chromatography devices and settings prior to modeling, such as the determination of column porosity. We also consider the challenges that must be overcome when models are set up and calibrated, including the cross-validation and verification of data-driven and hybrid (combined data-driven and mechanistic) models. This review will therefore support researchers intending to establish a chromatography modeling workflow in their laboratory.

Investigating the Trade-Off between Design and Operational Flexibility in Continuous Manufacturing of Pharmaceutical Tablets: A Case Study of the Fluid Bed Dryer

Market globalisation, shortened patent lifetimes and the ongoing shift towards personalised medicines exert unprecedented pressure on the pharmaceutical industry. In the push for continuous pharmaceutical manufacturing, processes need to be shown to be agile and robust enough to handle variations with respect to product demands and operating conditions. In this paper we examine the use of operational envelopes to study the trade-off between the design and operational flexibility of the fluid bed dryer at the heart of a tablet manufacturing process. The operating flexibility of this unit is key to the flexibility of the full process and its supply chain. The methodology shows that for the fluid bed dryer case study there is significant effect on flexibility of the process at different drying times with the optimal obtained at 700s. The flexibility is not affected by the change in volumetric flowrate, but only by the change in temperature. Here the method used a black box model to show how it could be done without access to the full model equation set, as this often needs to be the case in commercial settings.

Direct Compaction Drug Product Process Modeling

Most challenges during the development of solid dosage forms are related to the impact of any variations in raw material properties, batch size, or equipment scales on the product quality and the control of the manufacturing process. With the ever pertinent restrictions on time and resource availability versus heightened expectations to develop, optimize, and troubleshoot manufacturing processes, targeted and robust science-based process modeling platforms are essential. This review focuses on the modeling of unit operations and practices involved in batch manufacturing of solid dosage forms by direct compaction. An effort is made to highlight the key advances in the past five years, and to propose potentially beneficial future study directions.

Systematic Evaluation of the Effect of Formulation Variables on In Vitro Performance of Mometasone Furoate Suspension-Metered Dose Inhalers

The therapeutic benefits of metered dose inhalers (MDIs) in pulmonary disorders are mainly driven by aerosol performance, which depends on formulation variables (drug and excipients), device design, and patient interactions. The present study provides a comprehensive investigation to better understand the effect of formulation variables on mometasone furoate (MF) suspension-based MDI product performance. The effects of MF particle size (volume median diameter; X50) and excipient concentration (ethanol and oleic acid, cosolvent, and surfactant, respectively) on selected critical quality attributes (delivered dose (DD), fine particle dose of particles lesser than 5 µm (FPD < 5), ex-throat dose and median dissolution time (MDT)) were studied. Eight MF-MDI formulations (one per batch) were manufactured based on a reduced factorial design of experiment (DOE) approach, which included relevant formulation levels with varying X50 (1.1 and 2 μm), concentration of ethanol (0.45, 0.9, 1.8, and 3.6%w/w), and oleic acid (0.001 and 0.025%w/w). The in vitro evaluation of these MF-MDI formulations indicated the importance of drug particle's X50, oleic acid, and ethanol canister concentration as critical formulation variables governing the performance of MF suspension-based MDI products. The effect of these formulation variables on DD, FPD < 5, ex-throat dose, and MDT was subsequently utilized to develop empirical relationships linking formulation factors with effects on in vitro performance measures. The developed strategy could be useful for predicting MF-MDI product performance during MDI product development and manufacturing. The systematic DOE approach utilized in this study may provide insights into the understanding of the formulation variables governing the MF-MDI product performance.

Integrated Process Model Applications Linking Bioprocess Development to Quality by Design Milestones

Maximizing the value of each available data point in bioprocess development is essential in order to reduce the time-to-market, lower the number of expensive wet-lab experiments, and maximize process understanding. Advanced in silico methods are increasingly being investigated to accomplish these goals. Within this contribution, we propose a novel integrated process model procedure to maximize the use of development data to optimize the Stage 1 process validation work flow. We generate an integrated process model based on available data and apply two innovative Monte Carlo simulation-based parameter sensitivity analysis linearization techniques to automate two quality by design activities: determining risk assessment severity rankings and establishing preliminary control strategies for critical process parameters. These procedures are assessed in a case study for proof of concept on a candidate monoclonal antibody bioprocess after process development, but prior to process characterization. The evaluation was successful in returning results that were used to support Stage I process validation milestones and demonstrated the potential to reduce the investigated parameters by up to 24% in process characterization, while simultaneously setting up a strategy for iterative updates of risk assessments and process controls throughout the process life-cycle to ensure a robust and efficient drug supply.

Development of a Robust Control Strategy for Fixed-Dose Combination Bilayer Tablets with Integrated Quality by Design, Statistical, and Process Analytical Technology Approach

Control strategy and quality by design (QbD) are widely used to develop pharmaceutical products and improve drug quality; however, studies on fixed-dose combination (FDC) bilayer tablets are limited. In this study, the bilayer tablet consisted of high-dose metformin HCl in a sustained-release layer and low-dose dapagliflozin l-proline in an immediate-release layer. The formulation and process of each layer were optimized using the QbD approach. A d-optimal mixture design and response surface design were applied to optimize critical material attributes and critical process parameters, respectively. The robust design space was developed using Monte Carlo simulations by evaluating the risk of uncertainty in the model predictions. Multivariate analysis showed that there were significant correlations among impeller speed, massing time, granule bulk density, and dissolution in the metformin HCl layer, and among roller pressure, ribbon density, and dissolution in the dapagliflozin l-proline layer. Process analytical technology (PAT) was used with in–line transmittance near-infrared spectroscopy to confirm the bulk and ribbon densities of the optimized bilayer tablet. Moreover, the in vitro drug release and in vivo pharmacokinetic studies showed that the optimized test drug was bioequivalent to the reference drug. This study suggested that integrated QbD, statistical, and PAT approaches can develop a robust control strategy for FDC bilayer tablets by implementing real-time release testing based on the relationships among various variables.

Galenic Lab-on-a-Chip concept for lipid nanocapsules production

The continuous production of drug delivery systems assisted by microfluidics has drawn a growing interest because of the high reproducibility, low batch-to-batch variations, narrow and controlled particle size distributions and scale-up ease induced by this kind of processes. Besides, microfluidics offers opportunities for high throughput screening of process parameters and the implementation of process characterization techniques as close to the product as possible. In this context, we propose to spotlight the GALECHIP concept through the development of an instrumented microfluidic pilot considered as a Galenic Lab-on-a-Chip to formulate nanomedicines, such as lipid nanocapsules (LNCs), under controlled process conditions. In this paper we suggest an optimal rational development in terms of chip costs and designs. First, by using two common additive manufacturing techniques, namely fused deposition modelling and multi-jet modelling to prototype customized 3D microfluidic devices (chips and connectors). Secondly, by manufacturing transparent Silicon (Si)/Glass chips with similar channel geometries but obtained by a new approach of deep reactive ion etching (DRIE) technology suitable with in situ small angle X-ray scattering characterizations. LNCs were successfully produced by a phase inversion composition (PIC) process with highly monodispersed sizes from 25 nm to 100 nm and formulated using chips manufactured by 3D printing and DRIE technologies. The transparent Si/Glass chip was also used for the small angle X-ray scattering (SAXS) analysis of the LNC formulation with the PIC process. The 3D printing and DRIE technologies and their respective advantages are discussed in terms of cost, easiness to deploy and process developments in a GALECHIP point of view.

Investigating the Food and Drug Administration Biotherapeutics Review and Approval Process: Narrative Review

Background

The development, review, and approval process of therapeutic biological products in the United States presents two primary challenges: time and cost. Advancing a biotherapeutic from concept to market may take an average of 12 years, with costs exceeding US $1 billion, and the product may still fail the US Food and Drug Administration (FDA) approval process. Despite the FDA’s practices to expedite the approval of new therapies, seeking FDA approval remains a long, costly, and risky process.

Objective

The objective of this paper is to explore the factors and gaps related to the FDA review and approval process that contribute to process inefficiencies and complexities as well as proposed methods and solutions to address such gaps. This paper also aims to investigate the available modeling efforts for the FDA approval process of therapeutic biological products.

Methods

A narrative review of literature was conducted to understand the scope of published knowledge about challenges, opportunities, and specific methods to address the factors and gaps related to the review and approval of new drugs, including therapeutic biological products. Relevant peer-reviewed journal articles, conference proceedings, book chapters, official reports from public policy professional centers, and official reports and guidelines from the FDA were reviewed.

Results

Of the 23 articles identified in this narrative literature review, none modeled the current FDA review and approval process structure to address issues related to the robustness, reliability, and efficiency of its operations from an external point of view. Although several studies summarize the FDA approval process with clarity, in addition to bringing to light the problems and challenges faced by the regulatory agency, only a few attempts have been made to provide solutions for the problems and challenges identified. In addition, although several reform models have been discussed, these models lack the application of scientific methodologies and modeling techniques in understanding FDA as a complex sociotechnical system. Furthermore, tools and methods to assess the efficacy of the models before implementation are largely absent.

Conclusions

The findings suggest the efficacy of model-based systems engineering approaches for identifying opportunities for significant improvements to the FDA review and approval process. Using this holistic approach will serve several investigative purposes: identify influential sources of variability that cause major delays, including individual, team, and organizational decision making; identify the human-system bottlenecks; identify areas of opportunity for design-driven improvements; study the effect of induced changes in the system; and assess the robustness of the structure of the FDA approval process in terms of enforcement and information symmetry.

Solid Lipid Nanoparticles for Topical Drug Delivery: Mechanisms, Dosage Form Perspectives, and Translational Status

Solid lipid nanoparticles (SLNs) have shown potential as a novel lipid-based drug delivery system for the topical applications of innumerable therapeutic compounds. However, the mechanisms governing the absorption and cellular uptake of SLNs through topical route, along with the mechanism of drug release from SLNs are still ambiguous, and require further investigation. In addition, the selection of an appropriate dosage form/formulation base is essential for ease of application of SLNs and to enhance dermal and transdermal delivery. Upscaling and regulatory approvals are other challenges that may impede the clinical translation of SLNs. Therefore, this review focusses on different mechanisms involved in skin penetration and cellular uptake of SLNs. This is followed by a comprehensive discussion on the physicochemical properties of SLNs including various formulation and dosage form factors, which might influence the absorption of SLNs through the skin. Finally, translational status with respect to scale-up and regulatory aspects are also discussed. This review will be useful to researchers with an interest in topical applications of SLNs for the efficient delivery of drugs and cosmetics.

Control Strategy for Process Development of High-Shear Wet Granulation and Roller Compaction to Prepare a Combination Drug Using Integrated Quality by Design

In this study, we developed a control strategy for a drug product prepared by high-shear wet granulation and roller compaction using integrated quality by design (QbD). During the first and second stages, we optimized the process parameters through the design of experiments and identified the intermediate quality attributes (IQAs) and critical quality attributes (CQAs) relationship, respectively. In the first stage, we conducted an initial risk assessment by selecting critical process parameters with high impact on IQAs and CQAs and confirmed the correlation between control and response factors. Additionally, we performed Monte Carlo simulations by optimizing the process parameters to deriving and building a robust design space. In the second stage, we identified the IQAs and CQAs relationship for the control strategy, using multivariate analysis (MVA). Based on MVA, in the metformin layer, dissolution at 1 h was significantly correlated with intrinsic dissolution rate and granule size, and dissolution at 3 h was significantly correlated with bulk density and granule size. In dapagliflozin layer, dissolution at 10 min and 15 min was significantly correlated with granule size. Our results suggest that the desired drug quality may result through IQAs monitoring during the process and that the integrated QbD approach utilizing MVA can be used to develop a control strategy for producing high-quality drug products.

Multiparametric quality by design-fuzzy model applied in the development of a biomedical measuring system

This work presents the adaptation of the Quality by Design (QbD) approach for application in the quality assurance of a biomedical measuring system under development. First attempts in applying QbD to biomedical technologies indicated a significantly higher number of parameters than its traditional application in the pharmaceutical industry. These preliminary studies did not fulfill the QbD stage of Design Space (DS) configuration for biomedical devices, an essential step to identifying the proper operating ranges of parameters and guaranteeing quality features. Therefore, it persisted the challenge of configuring DS for health devices, overcoming dependences in the interaction of multiple process parameters and critical attributes. The present work develops a hybrid QbD-Fuzzy approach for multiparametric DS configuration. The proposed method was applied in the development phase of a low-cost and high-sensitive magnetic measuring system for locating metallic foreign bodies in patients, employing sensors based on the Giant Magnetoimpedance effect. The results provided the acceptable operating ranges of the multiple process parameters to ensure the biomedical equipment's suitability. The proposed strategy contributes to the QbD implementation in biomedical technologies and, therefore, promotes the reliability of diagnostic and therapeutic results in the clinical environment.

Quality by Design: Concept to Applications

BACKGROUND

Quality by Design (QbD) is associated with a modern, systematic, scientific and novel approach which is concerned with pre-distinct objectives that not only focus on product, process understanding but also lead to process control. It predominantly signifies the design and product improvement and the manufacturing process in order to fulfill the predefined manufactured goods or final products quality characteristics. It is quite essential to identify the desired and required product performance report, such as Target Product Profile, typical Quality Target Product Profile (QTPP) and Critical Quality Attributes (CQA).

METHODS

This review highlighted the concepts of QbD design space, for critical material attributes (CMAs) as well as the critical process parameters that can totally affect the CQAs within which the process shall be unaffected thus, consistently manufacturing the required product. Risk assessment tools and design of experiments are its prime components.

RESULTS

This paper outlines the basic knowledge of QbD, the key elements; steps as well as various tools for QbD implementation in pharmaceutics field are presented briefly. In addition to this, quite a lot of applications of QbD in numerous pharmaceutical related unit operations are discussed and summarized.

CONCLUSION

This article provides a complete data as well as the roadmap for universal implementation and application of QbD for pharmaceutical products.

Progressing Towards the Sustainable Development of Cream Formulations

This work aims at providing the assumptions to assist the sustainable development of cream formulations. Specifically, it envisions to rationalize and predict the effect of formulation and process variability on a 1% hydrocortisone cream quality profile, interplaying microstructure properties with product performance and stability. This tripartite analysis was supported by a Quality by Design approach, considering a three-factor, three-level Box-Behnken design. Critical material attributes and process parameters were identified from a failure mode, effects, and criticality analysis. The impact of glycerol monostearate amount, isopropyl myristate amount, and homogenization rate on relevant quality attributes was estimated crosswise. The significant variability in product droplet size, viscosity, thixotropic behavior, and viscoelastic properties demonstrated a noteworthy influence on hydrocortisone release profile (112 ± 2–196 ± 7 μg/cm²/√h) and permeation behavior (0.16 ± 0.03–0.97 ± 0.08 μg/cm²/h), and on the assay, instability index and creaming rate, with values ranging from 81.9 to 120.5%, 0.031 ± 0.012 to 0.28 ± 0.13 and from 0.009 ± 0.000 to 0.38 ± 0.07 μm/s, respectively. The release patterns were not straightforwardly correlated with the permeation behavior. Monitoring the microstructural parameters, through the balanced adjustment of formulation and process variables, is herein highlighted as the key enabler to predict cream performance and stability. Finally, based on quality targets and response constraints, optimal working conditions were successfully attained through the establishment of a design space.

Working within the Design Space: Do Our Static Process Characterization Methods Suffice?

The Process Analytical Technology initiative and Quality by Design paradigm have led to changes in the guidelines and views of how to develop drug manufacturing processes. On this occasion the concept of the design space, which describes the impact of process parameters and material attributes on the attributes of the product, was introduced in the ICH Q8 guideline. The way the design space is defined and can be presented for regulatory approval seems to be left to the applicants, among who at least a consensus on how to characterize the design space seems to have evolved. The large majority of design spaces described in publications seem to follow a "static" statistical experimentation and modeling approach. Given that temporal deviations in the process parameters (i.e., moving within the design space) are of a dynamic nature, static approaches might not suffice for the consideration of the implications of variations in the values of the process parameters. In this paper, different forms of design space representations are discussed and the current consensus is challenged, which in turn, establishes the need for a dynamic representation and characterization of the design space. Subsequently, selected approaches for a dynamic representation, characterization and validation which are proposed in the literature are discussed, also showcasing the opportunity to integrate the activities of process characterization, process monitoring and process control strategy development.

Digital Twins and Their Role in Model-Assisted Design of Experiments

Rising demands for biopharmaceuticals and the need to reduce manufacturing costs increase the pressure to develop productive and efficient bioprocesses. Among others, a major hurdle during process development and optimization studies is the huge experimental effort in conventional design of experiments (DoE) methods. As being an explorative approach, DoE requires extensive expert knowledge about the investigated factors and their boundary values and often leads to multiple rounds of time-consuming and costly experiments. The combination of DoE with a virtual representation of the bioprocess, called digital twin, in model-assisted DoE (mDoE) can be used as an alternative to decrease the number of experiments significantly. mDoE enables a knowledge-driven bioprocess development including the definition of a mathematical process model in the early development stages. In this chapter, digital twins and their role in mDoE are discussed. First, statistical DoE methods are introduced as the basis of mDoE. Second, the combination of a mathematical process model and DoE into mDoE is examined. This includes mathematical model structures and a selection scheme for the choice of DoE designs. Finally, the application of mDoE is discussed in a case study for the medium optimization in an antibody-producing Chinese hamster ovary cell culture process.

Industrial application of heat- and mass balance model for fluid-bed granulation for technology transfer and design space exploration

This work demonstrates the application of state-of-the-art modeling techniques in pharmaceutical manufacturing for fluid bed granulation at varying scales to successfully predict process conditions and ultimately replace experiments during a technology transfer of five products. We describe a mathematical model able to simulate the time-dependent moisture profile in a fluid bed granulation process. The applicability of this model is then demonstrated by calibrating and validating it over a range of operating conditions, manufacturing scales, and formulations. The inherent capability of the moisture profile to serve as a simple, scale-independent surrogate is shown by the large number of successful scale-ups and transfers. A methodology to use this 'digital twin' to systematically explore the effects of uncertainty inherent in the process input and model parameter spaces and their impact on the process outputs is described. Two case studies exemplifying the utilization of the model in industrial practice to assess process robustness are provided. Lastly, a pathway to leverage model results to establish proven acceptable ranges for individual parameters is outlined.

A Novel Framework to Aid the Development of Design Space across Multi-Unit Operation Pharmaceutical Processes-A Case Study of Panax Notoginseng Saponins Immediate Release Tablet

The fundamental principle of Quality by Design (QbD) is that the product quality should be designed into the process through an upstream approach, rather than be tested in the downstream. The keystone of QbD is process modeling, and thus, to develop a process control strategy based on the development of design space. Multivariate statistical analysis is a very useful tool to support the implementation of QbD in pharmaceutical process development and manufacturing. Nowadays, pharmaceutical process modeling is mainly focused on one-unit operations and system modeling for the development of design space across multi-unit operations is still limited. In this study, a general procedure that gives a holistic view for understanding and controlling the process settings for the entire manufacturing process was investigated. The proposed framework was tested on the Panax Notoginseng Saponins immediate release tablet (PNS IRT) production process. The critical variables and the critical units acting on the process were identified according to the importance of explaining the variability in the multi-block partial least squares path model. This improved understanding of the process by illustrating how the properties of the raw materials, the process parameters in the wet granulation and the compaction and the intermediate properties affect the tablet properties. Furthermore, the design space was developed to compensate for the variability source from the upstream. The results demonstrated that the proposed framework was an important tool to gain understanding and control the multi-unit operation process.

Robust Process Design in Pharmaceutical Manufacturing under Batch-to-Batch Variation

Model-based concepts have been proven to be beneficial in pharmaceutical manufacturing, thus contributing to low costs and high quality standards. However, model parameters are derived from imperfect, noisy measurement data, which result in uncertain parameter estimates and sub-optimal process design concepts. In the last two decades, various methods have been proposed for dealing with parameter uncertainties in model-based process design. Most concepts for robustification, however, ignore the batch-to-batch variations that are common in pharmaceutical manufacturing processes. In this work, a probability-box robust process design concept is proposed. Batch-to-batch variations were considered to be imprecise parameter uncertainties, and modeled as probability-boxes accordingly. The point estimate method was combined with the back-off approach for efficient uncertainty propagation and robust process design. The novel robustification concept was applied to a freeze-drying process. Optimal shelf temperature and chamber pressure profiles are presented for the robust process design under batch-to-batch variation.

Data fusion strategies for performance improvement of a Process Analytical Technology platform consisting of four instruments: An electrospinning case study

The aim of this work was to develop a PAT platform consisting of four complementary instruments for the characterization of electrospun amorphous solid dispersions with meloxicam. The investigated methods, namely NIR spectroscopy, Raman spectroscopy, Colorimetry and Image analysis were tested and compared considering the ability to quantify the active pharmaceutical ingredient and to detect production errors reflected in inhomogeneous deposition of fibers. Based on individual performance the calculated RMSEP values ranged between 0.654% and 2.292%. Mid-level data fusion consisting of data compression through latent variables and application of ANN for regression purposes proved efficient, yielding an RMSEP value of 0.153%. Under these conditions the model could be validated accordingly on the full calibration range. The complementarity of the PAT tools, demonstrated from the perspective of captured variability and outlier detection ability, contributed to model performance enhancement through data fusion. To the best of the author's knowledge, this is the first application of data fusion in the field of PAT for efficient handling of big-analytical-data provided by high-throughput instruments.

Scale-Up Strategy in Quality by Design Approach for Pharmaceutical Blending Process with Discrete Element Method Simulation

An approach combining quality by design (QbD) and the discrete element method (DEM) is proposed to establish an effective scale-up strategy for the blending process of an amlodipine formulation prepared by the direct compression method. Critical process parameters (CPPs) for intermediate critical quality attributes (IQAs) were identified using risk assessment (RA) in the QbD approach. A Box–Behnken design was applied to obtain the operating space for a laboratory-scale. A DEM model was developed by the input parameters for the amlodipine formulation; blending was simulated on a laboratory-scale V-blender (3 L) at optimal settings. The efficacy and reliability of the DEM model was validated through a comparison of simulation and experimental results. Change of operating space was evaluated using the validated DEM model when scaled-up to pilot-scale (10 L). Pilot-scale blending was simulated on a V-blender and double-cone blender at the optimal settings derived from the laboratory-scale operating space. Both pilot-scale simulation results suggest that blending time should be lower than the laboratory-scale optimized blending time to meet target values. These results confirm the change of operating space during the scale-up process. Therefore, this study suggests that a QbD-integrated DEM simulation can be a desirable approach for an effective scale-up strategy.

Raman Spectroscopy for Process Analytical Technologies of Pharmaceutical Secondary Manufacturing

As the process analytical technology (PAT) mindset is progressively introduced and adopted by the pharmaceutical companies, there is an increasing demand for effective and versatile real-time analyzers to address the quality assurance challenges of drug manufacturing. In the last decades, Raman spectroscopy has emerged as one of the most promising tools for non-destructive and fast characterization of the pharmaceutical processes. This review summarizes the achieved results of the real-time application of Raman spectroscopy in the field of the secondary manufacturing of pharmaceutical solid dosage forms, covering the most common secondary process steps of a tablet production line. In addition, the feasibility of Raman spectroscopy for real-time control is critically reviewed, and challenges and possible approaches to moving from real-time monitoring to process analytically controlled technologies (PACT) are discussed.

Design Space and QbD Approach for Production of Drug Nanocrystals by Wet Media Milling Techniques

Drug nanocrystals are nanosized solid drug particles, the most important application of which is the improvement of solubility properties of poorly soluble drug materials. Drug nanocrystals can be produced by many different techniques, but the mostly used are different kinds of media milling techniques; in milling, particle size of bulk sized drug material is decreased, with the aid of milling beads, to nanometer scale. Utilization of Quality by Design, QbD, approach in nanomilling improves the process-understanding of the system, and recently, the number of studies using the QbD approach in nanomilling has increased. In the QbD approach, the quality is built into the products and processes throughout the whole production chain. Definition of Critical Quality Attributes, CQAs, determines the targeted final product properties. CQAs are confirmed by setting Critical Process Parameters, CPPs, which include both process parameters but also input variables, like stabilizer amount or the solid state form of the drug. Finally, Design Space determines the limits in which CPPs should be in order to reach CQAs. This review discusses the milling process and process variables, CPPs, their impact on product properties, CQAs and challenges of the QbD approach in nanomilling studies.

Near infrared spectroscopy based monitoring of extraction processes of raw material with the help of dynamic predictive modeling

The control of batch-to-batch quality variations remains a challenging task for pharmaceutical industries, e.g., traditional Chinese medicine (TCM) manufacturing. One difficult problem is to produce pharmaceutical products with consistent quality from raw material of large quality variations. In this paper, an integrated methodology combining the near infrared spectroscopy (NIRS) and dynamic predictive modeling is developed for the monitoring and control of the batch extraction process of licorice. With the spectra data in hand, the initial state of the process is firstly estimated with a state-space model to construct a process monitoring strategy for the early detection of variations induced by the initial process inputs such as raw materials. Secondly, the quality property of the end product is predicted at the mid-course during the extraction process with a partial least squares (PLS) model. The batch-end-time (BET) is then adjusted accordingly to minimize the quality variations. In conclusion, our study shows that with the help of the dynamic predictive modeling, NIRS can offer the past and future information of the process, which enables more accurate monitoring and control of process performance and product quality.

Combining near infrared spectroscopy with predictive model and expertise to monitor herb extraction processes

Albeit extensively utilized, herb extraction process (HEP) is hard to be monitored because of its batch nature and the fluctuating quality of raw materials. Process analytical tools like near infrared spectroscopy (NIRS) can offer nondestructive examinations and collect abundant data of the process, which in principle contain the information about the quality of both the product and the process itself. However, extra effort is often required for the data mining of such process measurements, and extracting knowledge of the quality of process can be even harder. In this study, we take the extraction process of licorice as a typical HEP instance, and combine NIRS with classical partial least squared regression (PLSR) and expertise for its on-line monitoring. We show that our scheme effectively extracts information with clear physical meanings, through which we can even uncover the process fault that does not induce evident abnormalities in the product quality. Moreover, the constructed model can continuously evolve with more process data from daily operations, and the idea of the whole framework can be directly generalized to other HEP.