Process Design Space Research Articles

Effect of processing conditions and material attributes on the design space of lysozyme pellets prepared by extrusion/spheronization

The present work aimed to investigate the impact of the critical material attributes on the design space of the production of lysozyme pellets with suitable biological and physical properties for the subsequent coating process. The effect of two brands of both lysozyme and conformation stabilizing mannitol on the behavior of the composition in an extrusion/spheronization process was studied, while the experiments were designed according to 23 factorial design. The kneading of the mass was carried out in a high shear granulator equipped with a specially designed granulation chamber (Opulus Ltd, Hungary) constructed with seven built-in sensors for the measurement of temperature and relative humidity (RH). The special chamber is a novel tool for the identification of the critical points during processing a thermolabile drug by providing the online monitoring of critical environmental parameters and could be used to accurately determine the effect of critical process parameters and material attributes. The prepared samples were investigated for their biological and physical properties. It was found that the critical material attributes have a potential effect on the production process and product quality, and highly influence the size of the process design space. Therefore, the screening of the formulation materials is a key factor in macromolecular drug development.

Predicting process design spaces for spray drying amorphous solid dispersions

Amorphous solid dispersions (ASDs) are commonly manufactured using spray-drying processes. The product quality can be decisively influenced by the choice of process parameters. Following the quality-by-design approach, the identification of the spray-drying process design space is thus an integral task in drug product development. Aiming a solvent-free and homogeneous ASD, API crystallization and amorphous phase separation needs to be avoided during drying. This publication provides a predictive approach for determining spray-drying process conditions via considering thermodynamic driving forces for solvent drying as well as ASD-specific API/polymer/solvent interactions and glass transitions. The ternary API/polymer/solvent phase behavior was calculated using the Perturbed-Chain Statistical Associating Theory (PC-SAFT) and combined with mass and energy balances to find appropriate spray-drying conditions. A process design space was identified for the ASDs of ritonavir and naproxen with either poly(vinylpyrrolidone) or poly(vinylpyrrolidone-co-vinylacetate) spray dried from the solvents acetone, dichloromethane, or ethanol.

The Business Process Design Space for exploring process redesign alternatives

PurposeProcess redesign refers to the intentional change of business processes. While process redesign methods provide structure to redesign projects, they provide limited support during the actual creation of to-be processes. More specifically, existing approaches hardly develop an ontological perspective on what can be changed from a process design point of view, and they provide limited procedural guidance on how to derive possible process design alternatives. This paper aims to provide structured guidance during the to-be process creation.Design/methodology/approachUsing design space exploration as a theoretical lens, the authors develop a conceptual model of the design space for business processes, which facilitates the systematic exploration of design alternatives along different dimensions. The authors utilized an established method for taxonomy development for constructing the conceptual model. First, the authors derived design dimensions for business processes and underlying characteristics through a literature review. Second, the authors conducted semi-structured interviews with professional process experts. Third, the authors evaluated their artifact through three real-world applications.FindingsThe authors identified 19 business process design dimensions that are grouped into different layers and specified by underlying characteristics. Guiding questions and illustrative real-world examples help to deploy these design dimensions in practice. Taken together, the design dimensions form the “Business Process Design Space” (BPD-Space).Research limitations/implicationsPractitioners can use the BPD-Space to explore, question and rethink business processes in various respects.Originality/valueThe BPD-Space complements existing approaches by explicating process design dimensions. It abstracts from specific process flows and representations of processes and supports an unconstrained exploration of various alternative process designs.

Twin screw granulation: A simpler re-derivation of quantifying fill level.

The fill level is defined as the volume occupied by the powder and granules inside the twin-screw granulator in proportion to the maximum barrel channel void 'free' volume. In literature, the fill level is one of the key factors that determine the final granule properties as it relies on several factors such as the screw speed, screw element geometry, mass flow rate and barrel length. However, quantitative prediction of the fill level in twin-screw granulation (TSG) is still a developing area, which is required to enable effective development of process design space, to yield a product with desired quality attributes for all process scale levels (small to large equipment). In this study, a simple geometrical model is presented that predicts the barrel channel fill level in TSG. This model relates the volumetric flow rate to the forward volumetric conveying rate of the screws when they advance in the axial direction. Experimentation was conducted to validate the model by analytically measuring mass hold-up, the amount of material remaining in the barrel after steady state was reached, as the fill level is proportional to mass hold-up. Furthermore, the trends in the extent of granulation with the proposed model were investigated. Good agreement was found between the proposed fill level model and the mass hold-up for various screw elements, therefore the model provides a more practical measure of the fill level in TSG.

High-Throughput Exploration of the Process Space in 18% Ni (350) Maraging Steels via Spherical Indentation Stress–Strain Protocols and Gaussian Process Models

Several challenges are encountered in the development of maraging steels with desired combinations of mechanical properties. These include the need to explore a large process design space, the time- and effort-consuming standardized testing protocols for property evaluations, and the lack of a formal design of experiments strategy that guides the selection of the process conditions for the next set of experiments based on a thorough analyses of the previously accumulated data. In this work, we explored the effect of the different combinations of the aging temperature and the aging time on the yield strength of 350-grade maraging steels using high-throughput protocols. For this purpose, a total of 21 small volumes of differently aged samples were produced and studied using spherical nanoindentation stress–strain protocols. Furthermore, the results of these tests were modeled using Gaussian process regression (GPR) to establish data-driven linkages between the yield strengths and the aging parameters. The predicted yield strengths were found to be in reasonable agreement with the experimentally measured ones. It is also demonstrated that the GPR model can provide objective guidance in the selection of the next set of experiments.

Continuous twin screw granulation and fluid bed drying: A mechanistic scaling approach focusing optimal tablet properties.

This study provides the results of investigation on scaling approaches for three differently-sized continuous granulation lines, each consisting of a twin screw wet granulation process and a continuous fluid bed drying process. To check the initial scaling approach with regard to granule and tablet properties, a process parameter Design of experiment (DoE) was performed on each of the three equipment scales. The processed formulation did not contain cellulose to allow a high overall flowrate through the directly connected granulation and drying sections. Enhanced scaling aspects showed the influence of Froude number [-] at different twin screw granulator scales and screw speeds on the overgranulated particle fraction [% (V/V] as well as on the scale-dependent drying performance of the continuous fluid bed dryers. Scale-independent, specification limits of the two granule material attributes particle fine fraction [%] and residual water content [%] could be defined, resulting in high tableting performance in terms of tabletability and compressibility. Based on these specification limits and the statistical evaluation of the process parameter DoE, a process design space for the continuous granulation and drying process for each scale was calculated. It came up, that this process design space was decreasing in range with increasing equipment scale. The applicability of the presented scaling approach in terms of granule and tablet properties could successfully be demonstrated by three control experiments performed on the different equipment scales. In sum, this work delivers a basis for a smooth transition of scales within process development on the investigated continuous twin screw granulation and drying lines.

Optimization of the manufacturing process of a complex amphotericin B liposomal formulation using quality by design approach.

In this work, the manufacturing process of a complex liposomal amphotericin B (AmB) product was optimized using quality by design (QbD) approach. A comprehensive QbD-based process understanding and design space (DS) to the critical process parameters (CPPs) is essential to the drug development and consistent quality control. The process was based on the acid-aided formation of drug-lipid complexes in a methanol-chloroform mixture (step I) followed by spray drying (step II), hydration and liposome formation by microfluidization (step III), and lyophilization (step IV). Firstly, the risk assessment was conducted to identify the critical process parameters among the four key steps. Nine CPPs and five CQAs (API Monomer identity (absorbance main peak at 321nm), API Aggregation identity (absorbance peak ratio, OD 415nm/321nm), particle size, in-vitro toxicity, and the cake quality) were determined based on their severity and occurrences with their contribution to the quality target product profile (QTPP). Based on the risk assessment results, the final screening design of experiments (DoE) was developed using fractional factorial design. Secondly, the empirical equation was developed for each CQA based on experimental data. The impact of CPPs on the CQAs was analyzed using the coefficient plot and contour plot. In addition to the effect of individual formulation parameters and process parameters, the effects of the four key separate steps were also evaluated and compared. In general, the curing temperature during microfluidization has been identified as the most significant CPP. Finally, design space exploration was carried out to demonstrate how the critical process parameters can be varied to consistently produce a drug product with desired characteristics. The design space size increased at the higher value of the curing temperature, the API to phospholipid ratio (API:PL), and the lower value of the DSPG to phospholipid ratio (PG:PL) and aspirator rate.

Functional Data Analysis and Design of Experiments as Efficient Tools to Determine the Dynamical Design Space of Food and Biotechnological Batch Processes

A dynamical design space for the batch milling process of a hazelnut-and-cocoa-based paste in a stirred ball mill was obtained through functional data analysis (FDA) combined with design of experiments (DOE). A face-centred central composited design with two functional responses, fineness and energy, and three factors (rotational speed, mass of balls and ball diameter) was used. The functional responses were pre-processed, smoothed through B-spline approximation and subjected to functional principal component analysis (FPCA). The scores of the FPCA were modelled as a function of the experimental factors through response surface models and used to build a final model for the functional responses including only one principal component for energy and two for fineness. The developed models were able to predict with good accuracy the functional responses as a function of the experimental factors and time and allowed to build a dynamical design space. FDA combined with DOE appears to be an efficient and easy-to-use tool to model batch processes and obtain their dynamical design space.

Understanding and optimization of the secondary drying step of a freeze-drying process: a case study

The objective of this study was to build an understanding and optimize the secondary drying step of a freeze-drying process using a drug formulation from Fresenius Kabi (FK1) as a case study. For this purpose, a design of experiments (DoE) was developed to study the influence of drying temperature, drying time, vial position, and chamber pressure on moisture content and glass transition temperature of FK1. The results obtained herein showed that the influence of drying temperature on moisture content and glass transition temperature was strong at short drying time, implying that increasing drying temperature could lead to a significant decrease in the required drying time to achieve a designed moisture content and glass transition temperature. Variation in chamber pressure from 0.05-0.40 mbar showed insignificant influence on moisture content and glass transition temperature. Small variations in moisture content and glass transition temperature with respect to vial position were also observed, where edge vials had higher moisture content and, consequently, lower glass transition temperature than center vials. Further, the results from the DoE were utilized to construct a process design space (PDS) for the secondary drying step of FK1. Experimental verification of the PDS demonstrated an excellent agreement between the PDS-defined and experimentally-determined moisture content and glass transition temperature values. Thus, the PDS facilitated process optimization of FK1 and could serve as an important tool during scale-up and transfer of the process.

Optimal process design space to ensure maximum viability and productivity in Penicillium chrysogenum pellets during fed-batch cultivations through morphological and physiological control

BackgroundBiomass growth of Pencillium chrysogenum is characterised by a distinct pellet morphology consisting of compact hyphal agglomerates. Fungal pellets are advantageous in industrial process control due to rheological advantages but lead to biomass degradation due to diffusional limitations of oxygen and substrate in the pellet’s core. Several fermentation parameters are known to affect key pellet characteristics regarding morphology, viability and productivity. Pellet morphology and size are affected by agitation. Biomass viability and productivity are tightly interlinked with substrate uptake and dissolved oxygen concentration.ResultsThe goal of this study was to study the impact of the fermentation parameters power input, dissolved oxygen content and specific substrate uptake rate on morphology, biomass viability and productivity. A design of experiments (DoE) approach was conducted and corresponding responses were analysed using novel morphological descriptors analysed by a previously established flow cytometry method. Results clearly display inverse correlations between power input and pellet size, specific morphological parameters related to pellet density can be increased in direct proportion to power input. Biomass viability and productivity are negatively affected by high specific substrate uptake rates.ConclusionsBased upon multiple linear regression, it was possible to obtain an optimal design space for enhanced viability and productivity at beneficial morphological conditions. We could maintain a high number of pellets with favourable morphology at a power input of 1500 W/m3. A sound compromise between viability and high productivity is possible at a specific glucose uptake rate of 0.043 g/g/h at dissolved oxygen levels of 40% minimum.

Design space and robustness analysis of batch and counter-current frontal chromatography processes for the removal of antibody aggregates

Increasing molecular diversity and market competition requires biopharmaceutical manufacturers to intensify their processes. In this respect, frontal chromatography on cation exchange resins has shown its potential to effectively remove aggregates. However, yield losses during the wash step need to be accepted in order to ensure robust product quality. In this work, we present a novel counter-current frontal chromatography process called Flow2, which uses inline dilution during an interconnected wash phase to allow high monomer recovery without contaminating the product pool with impurities. Its model-based design spaces under purity and yield constraints are compared with those corresponding to traditional batch processes in terms of size and process attributes yield and productivity. The Flow2 process shows the largest extent of feasible operating points independent of feed conditions. Thereby, it allows the implementation of higher ionic strength wash, thus widening the range of operating conditions resulting in yields above 95% compared to batch processes. Productivities of batch and counter-current processes are the same at short regeneration times and equal residence time. However, long regeneration times, while influencing the size of the Flow2 design space, are not detrimental for its productivity resulting in twice as high values as obtained for the batch process. Furthermore, process robustness is evaluated by the ability of the process to maintain the required product quality when subjected to process parameter perturbations. It is found that the Flow2 process is able to retain a larger design space associated also with higher yields showing its ability to improve process attributes without sacrificing robustness at the same time.

Processing design space is critical for voriconazole nanoaggregates for dry powder inhalation produced by thin film freezing

We previously developed high-potency (up to 97%) crystalline voriconazole nanoaggregates intended for dry powder inhalation. A process using thin film freezing was designed such that nanoparticles of mannitol on the surface of voriconazole nanoaggregates modified the surface texture of the voriconazole nanoaggregates and thus the aerosolization properties of the powder. Identifying the process design space is absolutely critical for the development of the voriconazole nanoaggregates into a pharmaceutical product. Thus, we evaluated several critical parameters of the process design space, including: solvent system composition, processing temperature, solid loading, and scale. We also evaluated aerosolization of the powder with different devices, namely high and low resistance RS01 and RS00 DPIs.The solvent composition with a higher amount of water and a low processing temperature (−150 °C) was favorable to aerosolization. Higher solid loadings resulted in lower aerosol performance, while powder conditioning improved aerosolization. The 450-fold larger manufacture scale presented no differences in physicochemical and aerodynamic properties. While a low resistance RS00 device performed the best at a high flow rate, a high resistance RS00 device presented consistent aerosolization over the flow rate of 60 L/min and 30 L/min, and a dosing range of 10–20 mg per capsule.

Application of Gaussian process autoregressive models for capturing the time evolution of microstructure statistics from phase-field simulations for sintering of polycrystalline ceramics

While phase-field models have been demonstrated to be highly versatile in performing physics-based simulations of a large variety of materials phenomena involving microstructure evolution (e.g. phase transformation, recrystallization, sintering), they are not practical for rapid exploration of the process design space due to their high demand for computational resources. The extraction of reliable and robust reduced-order models from the microstructure evolution datasets produced by such sophisticated physics-based models continues to be an unsolved problem. Recent advances in the fast computation of a comprehensive set of microstructure statistics and their data-driven low-dimensional representations using principal component analyses have resulted in the successful extraction of practically useful reduced-order models connecting the microstructure statistics and the effective properties exhibited by the material. In this paper, we explore for the first time, the viability of these low-dimensional representations of the microstructure statistics for establishing reduced-order models capable of learning the important details of the microstructure evolution predicted by the computationally expensive phase-field models. More specifically, we will explore the viability of applying the Gaussian process autoregressive models used in the fields of statistics and signal processing for problems in microstructure evolution. This will be accomplished using a specific case study dealing with the time evolution of porous microstructures in sintering of polycrystalline ceramics.

A new generation of predictive models: The added value of hybrid models for manufacturing processes of therapeutic proteins.

Due to the lack of complete understanding of metabolic networks and reaction pathways, establishing a universal mechanistic model for mammalian cell culture processes remains a challenge. Contrarily, data-driven approaches for modeling these processes lack extrapolation capabilities. Hybrid modeling is a technique that exploits the synergy between the two modeling methods. Although mammalian cell cultures are among the most relevant processes in biotechnology and indeed looks ideal for hybrid modeling, their application has only been proposed but never developed in the literature. This study provides a quantitative assessment of the improvement brought by hybrid models with respect to the state-of-the-art statistical predictive models in the context of therapeutic protein production. This is illustrated using a dataset obtained from a 3.5 L fed-batch experiment. With the goal to robustly define the process design space, hybrid models reveal a superior capability to predict the time evolution of different process variables using only the initial and process conditions in comparison to the statistical models. Hybrid models not only feature more accurate prediction results but also demonstrate better robustness and extrapolation capabilities. For the future application, this study highlights the added value of hybrid modeling for model-based process optimization and design of experiments.

An Experimental-Based Approach to Construct the Process Design Space of a Freeze-Drying Process: An Effective Tool to Design an Optimum and Robust Freeze-Drying Process for Pharmaceuticals

The application of quality by design (QbD) is becoming an integral part of the formulation and process development for pharmaceutical products. An essential feature of the QbD philosophy is the design space. In this sense, a new approach to construct a process design space (PDS) for the primary drying section of a freeze-drying process is addressed in this paper. An effective customized design of experiments (DoE) is developed for freeze-drying experiments. The results obtained from the DoE are then used to construct the product-based PDS. The proposed product-based PDS construction approach has several advantages, including (1) eliminating assumptions on the heat transfer coefficient and dried product resistance, as it is constructed from experimental results specifically obtained from a given formulation, yielding more realistic and reliable results and (2) PDS construction based on a narrow range of product temperatures and considering the variations in product temperature and sublimation rate of vials across a shelf. This guarantees the effectiveness and robustness of the process and facilitates the process scale-up and transfer. The PDS developed herein was experimentally verified. The PDS predicted parameters were in excellent agreement with the experimentally obtained parameters.

Holistic QbD approach for hot-melt extrusion process design space evaluation: Linking materials science, experimentation and process modeling

The aim of this work was to investigate the relationship between formulation material properties, process parameters and process performance for the manufacturing of amorphous solid dispersions via hot-melt extrusion (HME) using experimentation coupled with process modeling. Specifically, we evaluated the impact of the matrix copovidone melt rheology with and without the addition of a plasticizing surfactant, polysorbate 80, while also varying the process parameters, barrel temperature and screw speed, and keeping fill volume constant. To correlate the process performance to a critical quality attribute, we used telmisartan as an indicator substance by processing at temperatures below its solubility temperature in the polymeric matrix. We observed a broader design space of HME processes for the plasticized formulation with respect to screw speed than for the copovidone-only matrix formulation. This observation was determined by the range of observed melt temperatures in the extruder, both measured and simulated. The reason was not primarily linked to a reduced shear-thinning behavior, characterized by the power law index, n, but instead more to an overall reduced melt viscosity during extrusion and zero-shear rate viscosity, η0, accordingly. We also found that the amount of residual crystallinity of telmisartan correlated with the simulated maximum melt temperature in the extruder barrel. This finding confirmed the applicability of the temperature-dependent API-matrix solubility phase diagram for HME to process development. Given the complex inter-dependent relationships between material properties, process and performance, process modeling combined with reduced laboratory experimentation was established as a holistic approach for the evaluation of Quality-by-Design-based HME process design spaces.

An Optimization-Based Framework to Define the Probabilistic Design Space of Pharmaceutical Processes with Model Uncertainty

To increase manufacturing flexibility and system understanding in pharmaceutical development, the FDA launched the quality by design (QbD) initiative. Within QbD, the design space is the multidimensional region (of the input variables and process parameters) where product quality is assured. Given the high cost of extensive experimentation, there is a need for computational methods to estimate the probabilistic design space that considers interactions between critical process parameters and critical quality attributes, as well as model uncertainty. In this paper we propose two algorithms that extend the flexibility test and flexibility index formulations to replace simulation-based analysis and identify the probabilistic design space more efficiently. The effectiveness and computational efficiency of these approaches is shown on a small example and an industrial case study.

Model-based Analysis and Optimisation of a Continuous Corynebacterium glutamicum Bioprocess Utilizing Lignocellulosic Waste

Lignocellulosic waste streams are an important sustainable alternative to conventional carbon sources for industrial biotechnology. However, lacking quantitative knowledge on cultivation behavior hampers process design and optimization. Using an unstructured kinetic model describing the growth of wild-type Corynebacterium glutamicum on a lignocellulosic waste stream from pulping industry, we designed a continuous fermentation process with an optimized space time yield for biomass of 0.86 g L-1 h-1 and a residual concentration of metabolizable sugars in the effluent of less than 2 %. The model considers the growth on multiple interacting sugars and potentially inhibitory effects of lignocellulosic waste. After parametrization on historical data, the model was used to determine optimal setpoints of dilution rate and feed medium concentration. Sensitivity analysis of the model provided additional information on the importance of certain parameters during different process conditions and detected bottlenecks of strain physiology limiting the process design space. This model-based approach delivers valuable insights for process and strain engineering already at an early stage of bioprocess development.

Application of near-infrared spectroscopy combined with design of experiments for process development of the pulsed spray fluid bed granulation process

The boundaries of a target product quality profile of certain failure modes can be exploited to construct the operation window, a process design space (PDS), for the granulation process. The purpose of this study is to implement process design and development for the pulsed spray fluid bed granulation (PSFBG) on a laboratory-scale granulator. This work consists of three parts: (1) implementation of Process Analytical Technology (PAT) combined with multivariate data analysis to monitor the formulation characteristics including moisture content, particle size and granule yield; (2) adoption of a new design of experiments (DoE) method Definitive Screening Design (DSD), with the well-established PAT monitoring tools, to investigate the process response to the intended changes in the critical process parameters (CPPs); and finally, (3) the definition of a PDS for the PSFBG, within which the acceptable performance of the process is guaranteed. The effects of the process parameters (i.e., the inlet air temperature, inlet air relative humidity, binder spray rate, atomization pressure, pulse cycle and pulse width) on granule size were evaluated. Two different failure modes were identified based on the measured target product quality (granule size) profile: risk of bed collapse due to over-wetting and low granulating efficiency. Using the constructed PDS for desired granulation, the expected final granule attributes can be obtained through properly controlling the process trajectory by adjusting the process parameters.

A novel and systematic approach to identify the design space of pharmaceutical processes

Feasibility analysis is a mathematical technique that can be used to assist the identification of the design space (DS) of a pharmaceutical process, given the availability of a process model. One of its main drawbacks is that it suffers from the curse of dimensionality, i.e. simulations can potentially become computationally extremely expensive and very cumbersome when the number of input factors is large. Additionally, giving a graphical and compact representation of the high-dimensional design space is difficult. In this study, we propose a novel and systematic methodology to exploit partial least-squares (PLS) regression modelling to reduce the dimensionality of a feasibility problem. We use PLS to obtain a linear transformation between the original multidimensional input space and a lower dimensional latent space. We then apply a Radial Basis Function (RBF) adaptive sampling feasibility analysis on this lower dimensional space to identify the feasible region of the process. We assess the accuracy and robustness of the results with three metrics, and we critically discuss the criteria that should be adopted for the choice of the number of latent variables. The performance of the methodology is tested on three simulated case studies, one of which involving the continuous direct compaction of a pharmaceutical powder. In all case studies, the methodology shows to be effective in reducing the computational burden while maintaining an accurate and robust identification of the design space.