Search Results (94)

This comprehensive review of the synergistic use of Quality by Design (QbD) and the Pi–Buckingham theorem explores an innovative approach to enhancing product development and process optimization within the pharmaceutical industry. QbD is a systematic, proactive methodology that integrates quality considerations throughout the product lifecycle to ensure that pharmaceutical products meet regulatory standards for safety and efficacy from the outset of development. The Pi–Buckingham theorem serves as a foundational principle in dimensional analysis, facilitating the simplification of complex models by transforming physical variables into dimensionless parameters. This synergy enables researchers to better understand and control the factors affecting critical quality attributes (CQAs), thereby improving manufacturing outcomes and minimizing variability. Full article

Background: Recombinant monoclonal antibodies represent a vital category of biologics, constituting the largest class of molecules used to treat autoimmune disorders, cancers, rheumatoid arthritis, and other chronic conditions. The IgG1 subclass is the most potent among all the immunoglobulin gamma (IgG) antibodies, inducing Fc-related effector functions. N-linked glycan distribution of therapeutic IgG1s affects Fc-related effector functions such as CDC (complement-dependent cytotoxicity) and ADCC (antibody dependent cell-mediated cytotoxicity) biological activities and efficacy in vivo. Hence, as a critical quality attribute (CQA), the glycosylation profile of therapeutic IgG1s must be consistently preserved, which is primarily influenced by manufacturing process factors. In the era of biosimilars, it is challenging for biopharmaceutical manufacturers to not only obtain the desired glycan distribution consistently but also to meet the innovator molecule specifications as per the regulatory agencies. Methods: This study investigates the CHO fed-batch process parameters that affect the titer and terminal galactosylation of the TNF-α blocker-IgG1. It was hypothesized that galactose supplementation would enhance the galactosylation of TNF-α blocker-IgG1. Results: It was observed that such in-cultivation process shift does not affect cell culture parameters yet significantly enhances the galactosylation of TNF-α blocker-IgG1. Interestingly, the results indicate that supplementing D-galactose from the exponential phase of the CHO fed-batch process had the greatest effect on Fc galactosylation, increasing the amount of total galactosylated TNF-α blocker-IgG1 from 7.7% to 15.8%. Conclusions: Our results demonstrate a relatively easy and viable technique for cell culture engineering that is more appropriate for industrial production than costly in vitro glycoengineering. Full article

Introduction: The use of response surface designs for drug formulation is highly warranted nowadays. Such smart designs reduce the number of required experiments compared to full-factorial designs, while providing highly accurate and reliable results. Aim: This study compares the effectiveness of two of the most commonly used response surface designs—Central Composite Design (CCD) and D-optimal Design (DOD)—in modeling a polymer-based drug delivery system. The performance of the two designs was further evaluated under a challenging scenario where a central point was deliberately converted into an outlier. Methods: Both methods were assessed using ANOVA, R-squared values, and adequate precision, and were assessed through an experimental validation point. Results: Both models demonstrated statistical significance (p-value < 0.05), confirming their ability to describe the relationships between formulation variables and critical quality attributes (CQAs). CCD achieved higher R-squared and predicted R-squared values compared to DOD (0.9977 and 0.9846 vs. 0.8792 and 0.7858, respectively), rendering it as the superior approach in terms of modeling complex variables’ interactions. However, DOD proved to be more predictive as it scored a lower percentage relative error. Conclusion: The demonstrated resilience of both models, despite the introduction of an outlier, further validates their utility in real-world applications, instead of the exhaustive full-factorial design. Full article

Background/Objectives: Controlling the critical quality attributes (CQAs), such as granule moisture level and particle size distribution, that impact product performance is essential for ensuring product quality in medicine manufacture. Oral solid dosage forms, such as tablets, often require appropriate powder flow for compaction and filling. Spray-dried fluidized bed granulation (FBG) is a key unit operation in the preparation of granulated powders. The determination of particle sizes in FBG using near-infrared spectroscopy (NIR) has been considered in the literature. Herein, for the first time, NIR is combined with process parameters to achieve improved prediction of the particle sizes in FBG. Methods: An inline model for particle size determination using both NIR and FBG process parameters was developed using the partial least square (PLS) method, or ‘merged-PLS model’. The particle size was predicted at the end point of the process, i.e., the last 10% of the particle-size data for each batch run. An additional two analyses included a merged-PLS model with 12 batches: (1) where nine batches were training and three batches were a test set; and (2) where 11 batches were training and one was a test batch. Results: For all considered particle size fractions, Dv10, Dv25, Dv50, Dv75, and Dv90, an improved root-mean-squared error of prediction (RMSEP) is obtained for the merged-PLS model compared to the NIR-only PLS model and compared to the process parameters alone model. Improved RMSEP is also achieved for the additional two analyses. Conclusions: The improved prediction performance of endpoint particle sizes by the merged-PLS model can help to enhance both the process understanding and the overall control of the FBG process. 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: Continuous manufacturing is gaining importance in the nanopharmaceutical field, offering improved process efficiency and product consistency. To fully leverage its potential, the integration of Process Analytical Technology (PAT) tools is essential for real-time quality control and robust process monitoring. Among the critical quality attributes (CQAs) of nanosystems, particle size plays a key role in ensuring product consistency and performance. However, real-time size monitoring remains challenging due to complex process dynamics and nanosystem heterogeneity. Methods: This study evaluates the applicability of conventional Dynamic Light Scattering (DLS) and spatially resolved DLS (SR-DLS) using the NanoFlowSizer (NFS) as PAT tools in a temperature-regulated top-down nano-production line. Various lipid-based nanosystems, including solid lipid nanoparticles (SLN), nanostructured lipid carriers (NLC), and nanoemulsions (NEs), were investigated. To ensure reliable implementation, key factors such as sample dilution, viscosity, focus position, measurement angle and temperature effects were systematically assessed for offline and at-line DLS using the Litesizer 500, as well as for offline, inline, and online SR-DLS using the NFS. Results: Offline screening confirmed that selecting the appropriate dilution medium and rate ensures measurement reliability. At-line methods provided an efficient alternative by enabling rapid final product control with minimal manual intervention. Inline and online monitoring further enhanced process efficiency by enabling real-time tracking of size, reducing waste, and allowing immediate process adjustments. Conclusions: This study demonstrates that integrating offline, at-line, in-line, and online DLS techniques allows for comprehensive product monitoring throughout the entire production line. This approach ensures a streamlined process, enables real-time adjustments, and facilitates reliable quality control after production and during storage. 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

Background/Objectives: Carbamazepine is widely used as a first-line treatment for pediatric patients with benign epilepsy. However, most commercial formulations have doses of 100 mg or higher, limiting their suitability for pediatric use. The aim of this study was to develop mini orally disintegrating tablets (ODTs) containing 50 mg of carbamazepine, utilizing direct compression technology, specifically tailored to meet the unique needs of pediatric patients. Methods: The development was carried out following a Quality by Design (QbD) approach, beginning with preformulation studies using the SeDeM expert system. Various co-processed excipients (PROSOLV^® ODT and PARTECK^® ODT) and non-co-processed excipients (L-HPC LH11 and L-HPC NBD-022) were evaluated. Additionally, modifications to the radius parameter of the SeDeM expert system were investigated to improve formulation design. Results: Optimized Formulations 13 and 14 achieved disintegration times below 1 min, hardness values between 25 and 60 N, and friability under 1%, fulfilling the predefined Critical Quality Attributes (CQAs). Tablets were successfully produced with a diameter of 5 mm and a weight below 100 mg. Moreover, reducing the SeDeM incidence radius from 5.0 to values between 4.0 and 3.5 proved viable, enabling the inclusion of excipients previously considered unsuitable and broadening formulation options without compromising quality. Conclusions: This study demonstrates the feasibility of producing small, fast-disintegrating, and mechanically robust 50 mg carbamazepine ODTs tailored for pediatric patients. It also validates the adjustment of SeDeM parameters as an effective strategy to expand excipient selection and enhance formulation flexibility in pediatric drug development. Full article

Background/Objectives: Quality by Design (QbD) has revolutionized pharmaceutical development by transitioning from reactive quality testing to proactive, science-driven methodologies. Rooted in ICH Q8–Q11 guidelines, QbD emphasizes defining Critical Quality Attributes (CQAs), establishing design spaces, and integrating risk management to enhance product robustness and regulatory flexibility. This review critically examines QbD’s theoretical frameworks, implementation workflows, and industrial applications, aiming to bridge academic research and commercial practices while addressing emerging challenges in biologics, advanced therapies, and personalized medicine. Methods: The review synthesizes regulatory guidelines, case studies, and multidisciplinary tools, including Design of Experiments (DoE), Failure Mode Effects Analysis (FMEA), Process Analytical Technology (PAT), and multivariate modeling. It evaluates QbD workflows—from Quality Target Product Profile (QTPP) definition to control strategies—and explores advanced technologies like AI-driven predictive modeling, digital twins, and continuous manufacturing. Results: QbD implementation reduces batch failures by 40%, optimizes dissolution profiles, and enhances process robustness through real-time monitoring (PAT) and adaptive control. However, technical barriers, such as nonlinear parameter interactions in complex systems, and regulatory disparities between agencies hinder broader adoption. Conclusions: QbD significantly advances pharmaceutical quality and efficiency, yet requires harmonized regulatory standards, lifecycle validation protocols, and cultural shifts toward interdisciplinary collaboration. Emerging trends, including AI-integrated design space exploration and 3D-printed personalized medicines, promise to address scalability and patient-centric needs. By fostering innovation and compliance, QbD remains pivotal in achieving sustainable, patient-focused drug development. Full article

Background: Chinese hamster ovary (CHO) cell metabolism is complex, influenced by nutrients like glucose and glutamine and metabolites such as lactate. Real-time monitoring is necessary for optimizing culture conditions and ensuring consistent product quality. Raman spectroscopy has emerged as a robust process analytical technology (PAT) tool due to its non-invasive, in situ capabilities. This study evaluates Raman spectroscopy for monitoring key metabolic parameters and IgG titer in CHO cell cultures. Methods: Raman spectroscopy was applied to five 10 L-scale CHO cell cultures. Partial least squares (PLS) regression models were developed from four batches, including one with induced cell death, to enhance robustness. The models were validated against blind test sets. Results: PLS models exhibited high predictive accuracy (R² > 0.9). Glucose and IgG titer predictions were reliable (RMSEP = 0.51 g/L and 0.12 g/L, respectively), while glutamine and lactate had higher RMSEP due to lower concentrations. Specific Raman bands contributed to the specificity of glucose, lactate, and IgG models. Predictions for viable (VCD) and total cell density (TCD) were less accurate due to the absence of direct Raman signals. Conclusions: This study confirms Raman spectroscopy’s potential for real-time, in situ bioprocess monitoring without manual sampling. Chemometric analysis enhances model robustness, supporting automated control systems. Raman data could enable continuous feedback regulation of critical nutrients like glucose, ensuring consistent critical quality attributes (CQAs) in biopharmaceutical production. Full article

Background: This study demonstrates the application of the sequential design of experiments (DoE) approach within the quality by design (QbD) framework to optimize extrusion processes through screening, optimization, and robustness testing. Methods: An in-line UV–Vis process analytical technology (PAT) system was successfully employed to monitor critical quality attributes (CQAs) of piroxicam amorphous solid dispersion (ASD) extrusion products, specifically lightness (L*). Results: L* measurement proved highly effective for ensuring the quality and uniformity of ASDs, offering real-time insights into their physical appearance and process stability. Small variations in L* acted as early indicators of processing issues, such as phase separation or bubble formation, enabling timely intervention. This straightforward and rapid technique supports real-time process monitoring and control, allowing automated adjustments to maintain product consistency and quality. By adopting this strategy, manufacturers can minimize variability, reduce waste, and ensure adherence to quality target product profiles (QTPPs). Conclusions: Overall, this study highlights the value of in-line UV–Vis spectroscopy as a PAT tool in hot melt extrusion, enhancing CQA assessment and advancing the efficiency and reliability of ASD manufacturing. Full article

Background/Objectives: This study aimed to establish a predictive model for critical quality attributes (CQAs) related to tablet integrity, including tablet breaking force (TBF), friability, and capping occurrence, using machine learning-based models and nondestructive experimental data. Methods: The machine learning-based models were trained on data to predict the CQAs of metformin HCl (MF)-containing tablets using a commercial-scale wet granulation process, and five models were each compared for regression and classification. We identified eight input variables associated with the process and material parameters that control the tableting outcome using feature importance analysis. Results: Among the models, the Gaussian Process regression model provided the most successful results, with R² values of 0.959 and 0.949 for TBF and friability, respectively. Capping occurrence was accurately predicted by all models, with the Boosted Trees model achieving a 97.80% accuracy. Feature importance analysis revealed that the compression force and magnesium stearate fraction were the most influential parameters in CQA prediction and are input variables that could be used in CQA prediction. Conclusions: These findings indicate that TBF, friability, and capping occurrence were successfully modeled using machine learning with a large dataset by constructing regression and classification models. Applying these models before tablet manufacturing can enhance product quality during wet granulation scale-up, particularly by preventing capping during the manufacturing process without damaging the tablets. Full article

Background/Objectives: Trastuzumab is an effective therapeutic intervention for treating HER2-positive breast cancers. The cost-effectiveness, global demand, and patent expiration of trastuzumab have led to the inflow of its biosimilars in the global market. With the rise of biosimilars in the biopharmaceutical market, it has become crucial to ensure that the biosimilar is at par with the original monoclonal antibody (mAb)in terms of efficacy, safety, and quality. Bioassay is one of the critical quality attributes (CQAs), hence developing a reliable and robust bioassay is essential for the evaluation of their biological activity and the harmonization of the quality of these biologics, supporting their safe and effective use in clinical practice. Methods: The present study aimed to develop a robust cell-based bioassay to assess the bioactivity of trastuzumab and its biosimilars for quality control testing. For this purpose, molecular characterization of different HER2-positive breast cancer cell lines of SKBR3, BT474, MDA-MD-453, MDA-MB-175, MCF-7, and MDA-MB-231 was performed to select a suitable cell line for the cell-based bioassay. Results: The SKBR3 cell line was found to express the HER2 receptors significantly higher in comparison to the other cell lines, and it was thereby selected for further bioassay optimization. The biological activity of trastuzumab was determined using the inhibition of proliferation (IOP) assay on the SKBR3, which was optimized based on the parameters of cell seeding density, drug dilution range, and incubation time, and it was further validated as per the compendial guidelines and found valid for the parameters of specificity, accuracy (% relative bias = 0.0067%), precision (repeatability: % GCV = 1.21%), linearity (R2 = 0.99), and range (50% to 200%). Additionally, the biological activity of different trastuzumab biosimilars was assessed using the validated IOP assay and compared to the HER2 binding assay performed by flow cytometry. The biological activity of different trastuzumab biosimilars was found to be comparable to the WHO primary reference standard of trastuzumab in terms of its relative potency using the IOP assay and binding assay by flow cytometry. Conclusions: Thus, an economic and robust cell-based bioassay method was successfully developed to assess the bioactivity of trastuzumab and its biosimilars. 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

Background: Active pharmaceutical ingredient (API) content is a critical quality attribute (CQA) of amorphous solid dispersions (ASDs) prepared by spraying a solution of APIs and polymers onto the excipients in fluid bed granulator. This study presents four methods for quantifying API content during ASD preparation. Methods: Raman and three near-infrared (NIR) process analysers were utilized to develop methods for API quantification. Four partial least squares (PLS) models were developed using measurements from three granulation batches, with an additional batch used to evaluate model predictability. Models performance was assessed using metrics such as root mean square error of prediction (RMSEP), root mean square error of cross-validation (RMSECV), residual prediction deviation (RPD), and others. Results: Off-line and at-line NIR models were identified as suitable for process control applications. Additionally, at-line Raman measurements effectively predicted the endpoint of the spraying phase. Conclusions: To the best of authors’ knowledge, this is the first study focused on monitoring API content during fluidized bed granulation (FBG) used for ASD preparation. The findings provide novel insights into the application of Raman and NIR process analysers with PLS modelling for monitoring and controlling ASD preparation processes. Full article