Search Results (41)

The Quality by Design (QbD) concept has been widely applied to the optimization of traditional Chinese medicine production processes recently. This work focused on optimizing the critical purification process of Scutellaria baicalensis extract used in the preparation of Zhusheyong Shuanghuanglian. Considering the impact of noise parameters and changes in herbal properties, an experimental design method was employed for optimization. Multiple batches of Scutellaria baicalensis decoction were prepared in this research, and quantitative models of Scutellaria baicalensis herbal properties, critical process parameters (CPPs), and process evaluation indicators were established. The R² of the quantitative models were all higher than 0.80. According to the model, the yield of baicalin was identified as a critical material property (CMA). The pH of first acid precipitation (X₁), first temperature holding time (X₂), pH of alkalization (X₃), ethanol amount (X₄), and end pH of ethanol washing (X₅) were CPPs. Considering the difficulty in controlling the end pH of the ethanol washing, it was considered to be a noise parameter. The Monte Carlo probability-based method was used to calculate the design space, determining the range of controllable parameters, which was successfully validated through experiments. Normal operation ranges for controllable parameters are recommended as follows: X₁ of 0.8–2.2, X₂ of 25–35 min, X₃ of 6.5–7.5, and X₄ of 0.8–1.2 g/g. Full article

Compressed air energy storage (CAES) systems represent a critical technological solution for addressing power grid load fluctuations by generating electrical power during peak load periods and storing energy during low load periods. As a prominent branch of CAES, isothermal compressed air energy storage (ICAES) systems have attracted significant research attention due to their elimination of requirements for high-temperature storage chambers and high-temperature compressors. Implementing constant-pressure operation in air storage reservoirs not only enhances energy storage density but also improves system safety. However, existing constant-pressure air storage methodologies necessitate supplementary infrastructure, such as high-pressure water reservoirs or elevated hydraulic columns, thereby escalating capital expenditures. This study introduces a novel constant-pressure air storage strategy for ICAES systems utilizing a linear-driven liquid piston mechanism. The proposed approach achieves constant-pressure air storage through the dual-mode operation strategies of buffer tanks (CBA and CBP modes) and hydraulic cylinders (CPP and CPW modes), eliminating the requirement for an auxiliary high-pressure apparatus or extensive civil engineering modifications. A prototype two-stage constant-pressure ICAES architecture was proposed, integrating low-pressure equipment with liquid pistons and providing detailed operational processes for preconditioning, energy storage, and power generation. A comprehensive mathematical model of the system is developed and validated through process simulation and performance characterization of a 100 kWh capacity system. It demonstrates that under operational conditions of 1 MPa of low pressure and 5 MPa of storage pressure, the system achieves an efficiency of 74.0% when the low-pressure equipment and liquid piston exhibit efficiencies of 85% and 90%, respectively. Furthermore, parametric analysis reveals a negative correlation between system efficiency and low-pressure parameters. Full article

Background/Objectives: The Self-Nanoemulsifying Drug Delivery System (SNEDDS) has been widely applied in oral drug delivery, particularly for poorly water-soluble compounds. The successful development of SNEDDS largely depends on the precise composition of its components. This narrative review provides an in-depth analysis of Quality by Design (QbD), Design of Experiment (DoE), and in silico approach applications in SNEDDS development. Methods: The review is based on publications from 2020 to 2025, sourced from reputable scientific databases (Pubmed, Science direct, Taylor and francis, and Scopus). Results: Quality by Design (QbD) is a systematic and scientific approach that enhances product quality while ensuring the robustness and reproducibility of SNEDDS, as outlined in the Quality Target Product Profile (QTPP). DoE was integrated into the QbD framework to systematically evaluate the effects of predefined factors, particularly Critical Material Attributes (CMAs) and Critical Process Parameters (CPP_S), on the desired responses (Critical Quality Attributes/CQA), ultimately leading to the identification of the optimal SNEDDS formulation. Various DoEs, including the mixture design, response surface methodology, and factorial design, have been widely applied to SNEDDS formulations. The experimental design facilitates the analysis of the relationship between CQA and CMA/CPP, enabling the identification of optimized formulations with enhanced biopharmaceutical, pharmacokinetic, and pharmacodynamic profiles. As an essential addition to this review, in silico approach emerges as a valuable tool in the development of SNEDDS, offering deep insights into self-assembly dynamics, molecular interactions, and emulsification behaviour. By integrating molecular simulations with machine learning, this approach enables rational and efficient optimization. Conclusions: The integration of QbD, DoE, and in silico approaches holds significant potential in the development of SNEDDS. These strategies enable a more efficient, rational, and predictive formulation process. Full article

Background/Objectives: Traditional Quality by Testing (QbT) strategies rely heavily on end-product testing and offer limited insight into how critical process parameters (CPPs) influence product quality. This increases the risk of variability and inconsistent outcomes. To overcome these limitations, this study aimed to implement a Quality by Design (QbD) approach to optimize the manufacturing process of clopidogrel tablets. Methods: A science- and risk-based QbD framework was applied to identify and prioritize key CPPs, intermediate critical quality attributes (iCQAs), and final product CQAs across key unit operations—pre-blending, dry granulation, post-blending, lubrication, and compression. Risk assessment tools and statistical design of experiments (DoE) were used to define proven acceptable ranges (PARs). Results: The study revealed strong correlations between CPPs and CQAs, allowing the definition of PARs and development of a robust control strategy. This led to improved manufacturing consistency, reduced variability, and enhanced process understanding. Conclusions: The QbD approach minimized reliance on end-product testing while ensuring high-quality outcomes. This study offers a novel QbD implementation tailored to the manufacturing challenges of clopidogrel tablets, providing a validated approach that integrates dry granulation CPPs with process-specific CQAs. These results demonstrate the value of QbD in achieving robust pharmaceutical manufacturing and meeting regulatory expectations. Full article

In recent years, with the increasing patient population, the need for complex and patient-centric medications has increased enormously. Traditional manufacturing techniques such as direct blending, high shear granulation, and dry granulation can be used to develop simple solid oral medications. However, it is well known that “one size fits all” is not true for pharmaceutical medicines. Depending on the age, sex, and disease state, each patient might need a different dose, combination of medicines, and drug release pattern from the medications. By employing traditional practices, developing patient-centric medications remains challenging and unaddressed. Over the last few years, much research has been conducted exploring various additive manufacturing techniques for developing on-demand, complex, and patient-centric medications. Among all the techniques, nozzle-based additive manufacturing platforms such as pressure-assisted microsyringe (PAM) and fused deposition modeling (FDM) have been investigated thoroughly to develop various medications. Both nozzle-based techniques involve the application of thermal energy. However, PAM can also be operated under ambient conditions to process semi-solid materials. Nozzle-based techniques can also be paired with the hot melt extrusion (HME) process for establishing a continuous manufacturing platform by employing various in-line process analytical technology (PAT) tools for monitoring critical process parameters (CPPs) and critical material attributes (CMAs) for delivering safe, efficacious, and quality medications to the patient population without compromising critical quality attributes (CQAs). This review covers an in-depth discussion of various critical parameters and their influence on product quality, along with a note on the continuous manufacturing process, quality by design, and future perspectives. Full article

Triclabendazole (TCB) is a well-established anthelmintic effective in treating fascioliasis, a neglected tropical disease. This study employs quality by design (QbD) to investigate the impact of TCB polymorphism and pharmacotechnical variables, from the development of immediate-release tablets to process optimization and green analysis. Critical process parameters (CPPs) and critical material attributes (CMAs), characterized by type of polymorph, composition of excipients (talc, lactose, cornstarch, and magnesium stearate), and compression force, were screened using a Plackett–Burman design (n = 24), identifying polymorphic purity and cornstarch as a CPP. To establish a mathematical model linking CPP to dissolution behaviour, a multiple linear regression (MLR) was applied to the training design (central composite design, n = 18). Simultaneously, a near-infrared spectroscopy coupled to partial least squares (NIR-PLSs) method was developed to analyze CPPs. An independent set of samples was prepared and analyzed using the NIR-PLSs model, and their dissolution profiles were also obtained. The PLSs model successfully predicted the CPPs in the new samples, yielding almost quantitative results (100 ± 3%), and MLR dissolution predictions mirrored the actual dissolution profiles (f2 = 85). In conclusion, the developed model could serve as a comprehensive tool for the development and control of pharmaceutical formulations, starting from the polymorphic composition and extending to achieve targeted dissolution outcomes. Full article

Within process development, numerous experimental studies are undertaken to establish, optimize and characterize individual bioprocess unit operations. These studies pursue diverse objectives such as enhancing titer or minimizing impurities. Consequently, Design of Experiment (DoE) studies are planned and analyzed independently from each other, making it challenging to interlink individual data sets to form a comprehensive overview at the conclusion of the development process. This paper elucidates the methodology for constructing a Life-Cycle-DoE (LDoE), which integrates data-driven process knowledge through design augmentations. It delves into the strategy, highlights the challenges encountered and provides solutions for overcoming them. The LDoE approach facilitates the augmentation of an existing model with new experiments in a unified design. It allows for flexible design adaptations as per the requirements of subject matter experts (SME) during process development, concurrently enhancing model predictions by utilizing all available data. The LDoE boasts a broad application spectrum as it consolidates all data generated within bioprocess development into a single file and model. The study demonstrates that the LDoE approach enables a process characterization study (PCS) to be performed solely with development data. Furthermore, it identifies potentially critical process parameters (pCPPs) early, allowing for timely adaptations in process development to address these challenges. Full article

In the past several decades, polymeric microparticles (MPs) have emerged as viable solutions to address the limitations of standard pharmaceuticals and their corresponding delivery methods. While there are many preclinical studies that utilize polymeric MPs as a delivery vehicle, there are limited FDA-approved products. One potential barrier to the clinical translation of these technologies is a lack of understanding with regard to the manufacturing process, hindering batch scale-up. To address this knowledge gap, we sought to first identify critical processing parameters in the manufacturing process of blank (no therapeutic drug) and protein-loaded double-emulsion poly(lactic-co-glycolic) acid MPs through a quality by design approach. We then utilized the design of experiments as a tool to systematically investigate the impact of these parameters on critical quality attributes (e.g., size, surface morphology, release kinetics, inner occlusion size, etc.) of blank and protein-loaded MPs. Our results elucidate that some of the most significant CPPs impacting many CQAs of double-emulsion MPs are those within the primary or single-emulsion process (e.g., inner aqueous phase volume, solvent volume, etc.) and their interactions. Furthermore, our results indicate that microparticle internal structure (e.g., inner occlusion size, interconnectivity, etc.) can heavily influence protein release kinetics from double-emulsion MPs, suggesting it is a crucial CQA to understand. Altogether, this study identifies several important considerations in the manufacturing and characterization of double-emulsion MPs, potentially enhancing their translation. Full article

Hydrophobic ion pairing (HIP) complexation was found to be an efficient approach in modulating the release and enhancing the stability and encapsulation of hydrophilic macromolecules such as proteins in hydrophobic nano/microcarriers. The present work strives to develop and optimize the preparation of the HIP complex of the antimicrobial enzyme lysozyme (LYZ) with the ion-pairing agent (IPA) sodium dodecyl sulphate (SDS) relying on the quality-by-design (QbD) approach. The quality target product profile (QTPP) includes the achievement of maximal lipophilicity in a reversible manner to enable the maintenance of biological activity. The related critical quality attributes (CQAs) were defined as complexation efficacy, complex stability, enzyme recovery and activity. Three risk assessment (RA) tools were used to identify and rank the critical process parameters (CPPs) and critical material attributes (CMAs). From this assessment, the pH of the medium, LYZ:SDS molar ratio and drying conditions were determined as high-risk factors that need to be investigated. To the best of our knowledge, for the first time, electrostatic titration was used as a smart approach to determine the optimum molar ratio at different pH values. Based on the predefined CQAs, pH 8 with an LYZ/SDS molar ratio of 1:8 was found to be the optimal condition for complexation efficiency and recovery (%) of a biologically active enzyme. A cost-effective drying process based on a ventilated oven was developed, which resulted in complex qualities comparable to those obtained by the commonly used freeze-drying method. In a nutshell, the optimum conditions for the preparation of the LYZ/SDS HIP complex were efficiently facilitated by the rational application of QbD principles and the utilization of efficient electrostatic titration and ventilated oven-drying methods. Full article

Microcarrier-based cell culture is a commonly used method to facilitate the growth of anchorage-dependent cells like MA 104 for antigen manufacturing. However, conventionally, static cell culture is employed for cell propagation before seeding the production bioreactor with microcarriers (MCs). This study demonstrates the effective replacement of the conventional method by serial subculturing on MCs with in situ cell detachment under optimal conditions in closed culture units. This study proves that MA 104 can be subcultured at least five times on Cytodex 1 MC without the need for separating cells and MC after cell harvest. Process parameters impacting cell growth were studied post in situ cell detachment in a scaled-down model. Optimization, using augmented Design of Experiments (DoE) combined with hybrid modeling, facilitated rapid screening of the design space for critical process parameters (CPPs). Optimized conditions included an inoculation density of >16 cells/bead, 3.5–4.5 g/L of Cytodex 1, and a controlled agitation speed, starting at N_js (minimum agitation speed) for the first day with a maximum increase of 25% thereafter. With these design spaces for CPPs, a cell density of 2.6 ± 0.5 × 10⁶ cells/mL was achieved after five days. This refined bioprocess methodology offers a reliable and efficient approach for seed training in stirred tank reactors, which is particularly beneficial for viral vaccine production. Full article

Quality by design (QbD) serves as a systematic approach to pharmaceutical development, beginning with predefined objectives and emphasizing an understanding of the product based on sound science and risk management. The purpose of this study is to utilize the QbD concept to develop a stable peptide-loaded long-acting injection formulation. An in-depth comprehension of peptide degradation mechanisms was achieved through forced degradation investigations, elucidating (acid) hydrolysis and oxidation as the primary degradation pathways for the peptide ACTY116. The quality built into the product was focused on risk assessment, for which the critical material attributes (CMAs) and critical process parameters (CPPs) associated with the critical quality attributes (CQAs) of each formulation were identified, leading to the development of the corresponding control strategies. CQAs for three LAI (long-acting injectable) formulations were enhanced by taking the right control strategies. The LAI formulation exhibiting the highest stability for ACTY116 was chosen for subsequent pharmacokinetic investigations in rats. The objective of addressing peptide chemical instability and in vivo long-acting release was achieved. For other molecules with susceptible functionalities like amide bonds, amino groups, and hydroxyl groups, the utilization of PLGA-based in situ gel as an LAI formulation for stabilizing molecules provides valuable insights. Full article

The material extrusion 3D printing process known as fused deposition modeling (FDM) has recently gained relevance in the additive manufacturing industry for large-scale part production. However, improving the real-time monitoring of the process in terms of its mechanical properties remains important to extend the lifespan of numerous critical applications. To enhance the monitoring of mechanical properties during printing, it is necessary to understand the relationship between temperature profiles and ultimate tensile strength (UTS). This study uses a cyber–physical production system (CPPS) to analyze the impact of four key thermal parameters on the tensile properties of polylactic acid (PLA). Layer thickness, printing speed, and extrusion temperature are the most influential factors, while bed temperature has less impact. The Taguchi L-9 array and the full factorial design of experiments were implemented along with the deposited line’s local fused temperature profile analysis. Furthermore, correlations between temperature profiles with the bonding strength during layer adhesion and part solidification can be stated. The results showed that layer thickness is the most important factor, followed by printing speed and extrusion temperature, with very close influence between each other. The lowest impact is attributed to bed temperature. In the experiments, the UTS values varied from 46.38 MPa to 56.19 MPa. This represents an increase in the UTS of around 17% from the same material and printing design conditions but different temperature profiles. Additionally, it was possible to observe that the influence of the parameter variations was not linear in terms of the UTS value or temperature profiles. For example, the increase in the UTS at the 0.6 mm layer thickness was around four times greater than the increase at 0.4 mm. Finally, even when it was found that an increase in the layer temperature led to an increase in the value of the UTS, for some of the parameters, it could be observed that it was not the main factor that caused the UTS to increase. From the monitoring conditions analyzed, it was concluded that the material requires an optimal thermal transition between deposition, adhesion, and layer solidification in order to result in part components with good mechanical properties. A tracking or monitoring system, such as the one designed, can serve as a potential tool for reducing the anisotropy in part production in 3D printing systems. Full article

Hydrogel-based drug delivery systems are of interest to researchers for many reasons, such as biocompatibility, high diversity, and the possibility of administration from different routes. Despite these advantages, there are challenges, such as controlling the drug release rate and their mechanical properties during the manufacturing of these systems. For this reason, there is a need for the production and development of such drug delivery systems with a scientific strategy. For this reason, the quality by design (QbD) approach is used for the development of drug delivery systems. This approach, by identifying the most effective factors in the manufacturing of pharmaceutical products and controlling them, results in a product with the desired quality with the least number of errors. In this review article, an attempt is made to discuss the application and method of applying this approach in the development of hydrogel-based drug delivery systems. So that for the development and production of these systems, according to the type of drug delivery system, what target characteristics should be considered (QTPP) and what factors, such as material properties (CMA) or process parameters (CPP), should be taken into account to reach the critical quality attributes of the product (CQA). Full article

Well-characterized and scalable downstream processes for the purification of biologics are extremely demanding for delivering quality therapeutics to patients at a reasonable price. Erythropoietin (EPO) is a blockbuster biologic with diverse clinical applications, but its application is limited to financially well-off societies due to its high price. The high price of EPO is associated with the technical difficulties related to the purification challenge to obtain qualified products with a cost-effective defined process. Though there are reports for the purification of EPO there is no report of a well-characterized downstream process with critical process parameters (CPPs) that can deliver EPO consistently satisfying the quality target product profile (QTPP), which is a critical regulatory requirement. To advance the field, we applied the quality by design (QbD) principle and design of experiment (DoE) protocol to establish an effective process, which is scalable up to 100× batch size satisfying QTPP. We have successfully transformed the process from static mode to dynamic mode and validated it. Insignificant variation (p > 0.05) within and between 1×, 10×, and 100× batches showed that the process is reproducible and seamlessly scalable. The biochemical analysis along with the biofunctionality data ensures that the products from different scale batches were indifferent and comparable to a reference product. Our study thereby established a robust and scalable downstream process of EPO biosimilar satisfying QTPP. The technological scheme presented here can speed up the production of not only EPO but also many other life-saving biologics and make them available to the mass population at a reduced cost. Full article

Hybrid modeling workflows combining machine learning with mechanistic process descriptions are becoming essential tools for bioprocess digitalization. In this study, a hybrid deep modeling method with state–space reduction was developed and showcased with a P. pastoris GS115 Mut+ strain expressing a single-chain antibody fragment (scFv). Deep feedforward neural networks (FFNN) with varying depths were connected in series with bioreactor macroscopic material balance equations. The hybrid model structure was trained with a deep learning technique based on the adaptive moment estimation method (ADAM), semidirect sensitivity equations and stochastic regularization. A state–space reduction method was investigated based on a principal component analysis (PCA) of the cumulative reacted amount. Data of nine fed-batch P. pastoris 50 L cultivations served to validate the method. Hybrid deep models were developed describing process dynamics as a function of critical process parameters (CPPs). The state–space reduction method succeeded to decrease the hybrid model complexity by 60% and to improve the predictive power by 18.5% in relation to the nonreduced version. An exploratory design space analysis showed that the optimization of the feed of methanol and of inorganic elements has the potential to increase the scFv endpoint titer by 30% and 80%, respectively, in relation to the reference condition. Full article