Expanding the term “Design Space” in high performance liquid chromatography (I)

A state-of-the-art ultra-performance liquid chromatographic (UPLC) method has been developed for purity testing of carbamazepine. Successful chromatographic separation of the active pharmaceutical ingredient (API) from its impurities was achieved on a WATERS ACQUITY UPLC CSH C18 column with the dimensions 2.1 mm × 100 mm and 1.7 µm particle size with gradient elution of 0.2% phosphoric acid and acetonitrile in only 5 min. Incorporating Quality by Design (QbD) principles to the method development approach by using the statistical software package Fusion AE allows the study of the relationship between chromatographic parameters (factors) and the resolution (response) between the peaks of interest. In a screening phase, the factors known to have a major effect on column selectivity (stationary phase, pH of the aqueous eluent, organic eluent type, gradient time, and slope) were studied. In the second phase, the chromatographic parameters that were identified as affecting the resolution were studied with additional instrument settings. In both phases, statistical concepts with experimental design plans (Design-of-Experiments) are used as an efficient and fast tool to simultaneously gain knowledge regarding the influencing factors and interactions. An operating space within the design space was established and a verification study confirmed the robustness of the final method. Total analysis time was only 5 min, which is an impressive 22-fold increase in productivity in comparison to the method published in the European Pharmacopeia.

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.

BackgroundAnalytical Quality by Design (AQbD) has emerged as a pivotal paradigm in the realm of analytical chemistry, revolutionizing the approach to method development and validation. This innovative strategy integrates principles of Quality by Design (QbD) into analytical procedures, aiming to ensure the quality and robustness of analytical methods. The traditional approach to method development often involves a trial-and-error process, where parameters are adjusted until satisfactory results are obtained. However, this approach can be time-consuming, resource-intensive, and may lack reproducibility. AQbD addresses these challenges by providing a systematic framework for method development that emphasizes understanding the relationship between critical process parameters (CPPs) and critical quality attributes (CQAs).DiscussionAnalytical Quality by design (AQbD) presents an innovative approach to creating and validating analytical procedures, aimed at achieving quality measurements within the method operable design region (MODR). The QbD approach to analytical development is proactive, methodical, and risk-based significantly helps in acquiring an in-depth knowledge of how critical process parameters (CPPs) affect analytical performances, measured by critical quality attributes (CQAs). Experiment design (DoE) is an essential part of QbD, functioning to carry out the response surface analysis and screening process, and ultimately to enable the definition of the multidimensional region of the successful operating ranges of the CPPs, known as the design space (DS). The purpose of this article is to offer a thorough description of the QbD approach's method development process and its implementation in analytical procedure validation, to produce high-quality output, employing statistical analysis in conjunction with other designing tools.ConclusionAnalytical Quality by design (AQbD) offers a systematic and proactive approach to method development in analytical procedures. By focusing on method resilience and incorporating statistical analysis and experiment design (DoE), AQbD enables the production of high-quality outputs, while minimizing out of trend (OOT) and out of specification (OOS) results. This innovative strategy enhances the reliability and reproducibility of analytical methods, ultimately leading to improved outcomes across various industries.

The philosophy of quality by design (QbD) is now leading the changes in the drug manufacturing mode from the conventional test-based approach to the science and risk based approach focusing on the detailed research and understanding of the production process. Along with the constant deepening of the understanding of the manufacturing process, the design space will be determined, and the emphasis of quality control will be shifted from the quality standards to the design space. Therefore, the establishment of the design space is core step in the implementation of QbD, and it is of great importance to study the methods for building the design space. This essay proposes the concept of design space for the production process of traditional Chinese medicine (TCM) preparations, gives a systematic introduction of the concept of the design space, analyzes the feasibility and significance to build the design space in the production process of traditional Chinese medicine preparations, and proposes study approaches on the basis of examples that comply with the characteristics of traditional Chinese medicine preparations, as well as future study orientations.

Mathematical models can be used as an integral part of the quality by design (QbD) concept throughout the product lifecycle for variety of purposes, including appointment of the design space and control strategy, continual improvement and risk assessment. Examples of different mathematical modeling techniques (mechanistic, empirical and hybrid) in the pharmaceutical development and process monitoring or control are provided in the presented review. In the QbD context, mathematical models are predominantly used to support design space and/or control strategies. Considering their impact to the final product quality, models can be divided into the following categories: high, medium and low impact models. Although there are regulatory guidelines on the topic of modeling applications, review of QbD-based submission containing modeling elements revealed concerns regarding the scale-dependency of design spaces and verification of models predictions at commercial scale of manufacturing, especially regarding real-time release (RTR) models. Authors provide critical overview on the good modeling practices and introduce concepts of multiple-unit, adaptive and dynamic design space, multivariate specifications and methods for process uncertainty analysis. RTR specification with mathematical model and different approaches to multivariate statistical process control supporting process analytical technologies are also presented.

The purpose of this study was to implement Quality by Design (QbD) concept to Solid Lipid Nanoparticles (SLN) containing Asenapine maleate (AM) in order to identify critical process and formulation variables which can affect product quality such as particle size (PS) and entrapment efficiency (EE). Initially, risk assessment using ishikawa diagram and preliminary investigation of critical variables was carried out. Two statistical designs were used to optimize critical variables which can affect product quality attributes i.e. PS and EE. Plackett Burman Design (PBD) was used to screen 8 variables and results showed that lipid concentration, surfactant concentration and sonication time had significant effect on PS and EE. These critical factors were further optimized using Central Composite Design (CCD), a type of response surface methodology, to assess its effect on PS and EE. Design space was identified and implementation of control strategy for responses generated quality of the desired product. Design space was generated for SLN for reducing intra-batch and inter-batch variability in formulation development process. Analysis of robustness of design space predicted that the formulation must be prepared in established design space to reduce batch variations. The results conclusively demonstrated the potential of QbD concept to build quality in SLN formulation.

Pharmaceutical Quality by Design

This study explains about the Analytical Quality by Design approach for the optimization of a High-Performance Liquid Chromatography Method for the simultaneous estimation of Metformin and Ertugliflozin in pharmaceutical substance. The study aimed to optimize the High-Performance Liquid Chromatography (HPLC) by means of an analytical target profile in order to achieve good separation of compounds along with acceptable analysis time. Identification of risk factors for variables affects the method efficacy. This leads to the development of an accurate, precise, and economic method. The optimized conditions of the developed method were a stationary phase of a Discovery C18 250 x 4.6mm, 5m and a mobile phase of Orthophosphoric acid buffer (pH 2.2),ACN taken in the ratio 60:40 was selected as mobile phase and detection wavelength of 230nm. The flow rate was selected as 0.98ml/min at 29.150C column temperature. Using the central composite design (CCD) method was optimized. The method is showing the linearity over the concentration range of 25-150µg/ml for Metformin and 0.375-2.25µg/ml for Ertugliflozin. The intra-and inter-day precision were less than 2% of relative standard deviation. Accuracies between 99-102% of the true values.The LOD obtained for Metformin and Ertugliflozin were found to be 59 and 3.7, respectively. LOQ obtained for Metformin and Ertugliflozin were 77.6 and 5.2, respectively.Under accelerated conditionsdegradation percentage of the drug was found to be less than 10%, and the degradation product peak not affecting the system suitability of Metformin and Ertugliflozin.

The need for consistency in analytical method development reinforces the dependence of pharmaceutical product development and manufacturing on robust analytical data. The Analytical Quality by Design (AQbD), akin to the product Quality by Design (QbD) endows a high degree of confidence to the method quality developed. AQbD involves the definition of the analytical target profile as starting point, followed by the identification of critical method variables and critical analytical attributes, supported on risk assessment and design of experiment tools for the establishment of a method operable design region and control strategy of the method. This systematic approach moves away from reactive troubleshooting to proactive failure reduction. The objective of this review is to highlight the elements of the AQbD framework and provide an overview of their implementation status in various analytical methods used in the pharmaceutical field. These methodologies include but are not limited to, high-performance liquid chromatography, UV-Vis spectrophotometry, capillary electrophoresis, supercritical fluid chromatography, and high-performance thin-layer chromatography. Finally, a critical appraisal is provided to highlight how regulators have encouraged AQbD principles application to boost the prevention of method failures and a better understanding of the method operable design region (MODR) and control strategy, ultimately resulting in cost-effectiveness and regulatory flexibility.

Although quality by design (QbD) is widely accepted and put into practice for chemical process and formulation development, little attention has been paid to apply it to the analytical methods development and validation. A proposal of applying a science-based QbD approach to a High Performance Liquid Chromatographic (HPLC) analytical method development is presented in this paper. Using the JMP software for DOE design and data analysis, critical method parameters can be identified and thus proper ranges can be set in order to ensure the robustness of the method. This approach allows the robustness to be built into the method during the step of method development.

ABSTRACT: Medical device manufacturing is increasingly challenged by complex design requirements, stringent regulatory frameworks, and the need for cost-efficient production. Two established methodologies—Design for Manufacturability (DfM) and Quality by Design (QbD)—offer complementary approaches to addressing these challenges. DfM emphasizes optimizing product designs for efficient, scalable, and cost-effective production, while QbD provides a systematic, risk-based approach to embedding quality, safety, and regulatory compliance throughout the development process. Despite their strengths, these methodologies are often applied independently, which limits their potential to transform medical device innovation. This research proposes a synergistic framework that integrates DfM and QbD to optimize product development in medical device manufacturing. The framework is developed through a structured analysis of literature and regulatory practices, mapping the overlap of DfM and QbD within the stage-gate development process. The results highlight that combining these methodologies reduces time-to-market, lowers defect rates, improves compliance with international standards, and enhances human factors engineering. The integration further supports advanced manufacturing techniques such as additive manufacturing, immersive technologies, and combination products. The contributions of this research are threefold. Theoretically, it advances systems-thinking approaches by uniting design efficiency with risk-based quality assurance. Practically, it provides medical device manufacturers with a roadmap for balancing cost efficiency, compliance, and patient safety. From a regulatory perspective, it offers guidance on harmonizing design and quality frameworks across global markets. Ultimately, the integration of DfM and QbD is positioned not only as a best practice but as a strategic imperative for driving sustainable innovation in medical device manufacturing.

The integration of artificial intelligence (AI) into QbD frameworks has further revolutionized this process, enabling enhanced efficiency, precision, and innovation in formulation development. This review explores the role of AI in advancing QbD for pharmaceutical formulations, highlighting its applications, benefits, challenges, and future potential. By analyzing recent advancements, Our goal is to present a thorough grasp of how AI-driven QbD is influencing medication development going forward. Quality by Design (QbD) frameworks that incorporate artificial intelligence (AI) offer a revolutionary method for developing pharmaceutical formulations..This review explores how AI-driven tools enhance the QbD process by optimizing formulation design, predicting critical quality attributes (CQAs), and streamlining risk assessment. By utilizing machine learning models, AI enables accurate determination of essential material characteristics (EMCs) and vital process parameters (VPPs), reducing experimental trial-and-error. The abstract highlights case studies where AI has improved formulation stability, bioavailability, and manufacturing efficiency. Furthermore, it discusses the potential of AI to integrate real-time data analytics for continuous process verification, ensuring robust quality control. The authors emphasize that AI-augmented QbD accelerates development timelines.

The current studies entail systematic quality by design (QbD)-based development of a simple, rapid, sensitive and cost-effective stability-indicating method for the estimation of olmesartan medoxomil. Quality target method profile was defined and critical analytical attributes (CAAs) for the reverse-phase liquid chromatography method earmarked. Chromatographic separation accomplished on a C18 column using acetonitrile and water (containing 0.1% orthophosphoric acid, pH 3.5) in 40 : 60 (v/v) as mobile phase at a flow rate of 1.0 mL/min with UV detection at 243 nm. Risk assessment studies and screening studies facilitated comprehensive understanding of the factors affecting CAAs. The mobile phase ratio and flow rate were identified as critical method parameters (CMPs) and were systematically optimized using face-centered cubic design, evaluating for CAAs, namely peak area, retention time, theoretical plates and peak tailing. Statistical modelization was accomplished followed by response surface analysis for comprehending plausible interaction(s) among CMPs. Search for optimum solution was conducted through numerical and graphical optimization for demarcating the design space. Analytical method validation and subsequent forced degradation studies corroborated the method to be highly efficient for routine analysis of drug and its degradation products. The studies successfully demonstrate the utility of QbD approach for developing the highly sensitive liquid chromatographic method with enhanced method performance.

Optimization of the quality by design approach for gene therapy products: A case study for adeno-associated viral vectors

Objective: The purpose of this study was an implementation of quality by design (QbD) for the formulation and characterization of econazole nitrate loaded flexisomal nanocarriers (EN-FS) to improve antifungal activity and to enhance skin permeability. Methods: Initially quality target product profile (QTTP) elements were identified and later critical quality attributes (CQA) elements were defined from QTPP elements. Particle size, entrapment efficiency and deformability index were defined as CQAs. Risk assessment was performed by using fishbone diagram and failure mode and effect analysis (FMEA). Design space was created by using fractional factorial design 25-2 as screening design and 32 full factorial design as optimization design to optimize two variables soya phosphatidylcholine concentration(X1) and sodium deoxycholate concentration (X2). Results: Optimized batch of EN-FS was 249.5±3.48 nm, with entrapment efficiency of 88.6±0.89 % and deformability index of 31.75±0.98. Zeta potential analysis showed value of-22.5 mV. Morphological analysis by TEM showed spherical shaped flexisomes, which confirmed the vesicular characteristics. The optimized batch of EN-FS was further made into hydrogels by using sepineo P600 as gelling agent. The % drug diffusion of EN-FS hydrogels had shown 24.68%. With higher skin deposition and higher value of zone of inhibition in antifungal study as compared to plain EN hydrogel. CLSM studies indicated deep penetration of EN-FS in skin layers. Lastly control strategy for EN-FS were developed. Conclusion: It was concluded that EN-FS showed high flexibility and enhanced antifungal activity therefore found to be a potential nanocarriers for drug deposition in skin layers without disturbing skin integrity.