AI-Enable Quality by Design in Pharmaceutical Formulation

Objective: To develop a validated Liquid Chromatography-Mass Spectrometry (LC-MS) method through Quality by Design (QbD) approach for the determination of tenofovir disoproxil fumarate (tenofovir DF) in bulk, pharmaceutical formulation and stress conditions.
 Methods: The analytical target profile (ATP) was to develop a simple, accurate, precise, specific, and robust method with the Critical Quality Attribute (CQA) being retention time, theoretical plate and peak tailing. Derived from the risk assessment studies, four high-risk factors were screened using resolution V irregular fraction design. Organic modifier strength, flow rate and injection volume were further optimized using Box-Behnken design (BBD). The optimized method condition was validated and applied for the determination of the analyte in pharmaceutical formulation. The stability of the analyte in stress conditions was determined. All experiments were performed using reverse-phase Acquity UPLC BEH C18 (1.7 µm, 1 mm X 50 mm) column coupled in-line with Synapt G2 mass spectrometer in positive electrospray ionization (ESI) mode with electron multiplier analog-to-digital detector.
 Results: Optimized method condition eluted analyte at 3.40 min and the m/z 520 ratified the analyte peak. The method was validated and was found to be specific, linear in the range of 20-100 µg/ml (R2 = 0.9998) with accuracy between 98.71% - 101.17% and precise. The limit of detection and quantification were 5.50 µg/ml and 16.68 µg/ml respectively. Analyte degraded completely in acid and basic environment, however, stable in oxidative and photolytic conditions.
 Conclusion: The validated method developed through the systematic and rational QbD approach bespoke superior quality. The QbD approach not only justified the entire process but also eliminated uncertainties.

The integration of artificial intelligence (AI) in education has the potential to revolutionize teaching and learning, particularly in the development of students’ critical thinking skills. This study explores science instructors' familiarity, perceptions, and experiences with using AI to enhance students' critical thinking skills, as well as the level of institutional support for AI integration in teaching. A quantitative survey was conducted among 20 science instructors from higher education institutions in Isabela, Philippines. The findings reveal that while instructors acknowledge AI's potential to improve educational outcomes, there is a significant gap in formal AI training and literacy among educators. Positive correlations were found between AI literacy, AI integration, and critical thinking development, suggesting that as AI literacy increases, AI integration and enhancement of critical thinking skills also increase. Regression analysis identified AI integration as a significant predictor of critical thinking development. Challenges remain in the effective implementation of AI, including concerns about overreliance on AI-generated responses and the need for clear assessment guidelines. Interestingly, years of teaching experience did not significantly influence participants’ AI literacy, perceptions, or integration. This study highlights the importance of developing comprehensive AI literacy programs for educators and integrating AI into curriculum structures to balance AI-enhanced learning with human-centered pedagogy. These findings emphasize the need for thoughtful implementation and ongoing research to effectively leverage AI in promoting critical thinking skills in science education.

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.

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.

Quality by design (QbD) refers to a new approach to product development that could increase efficiencies, provide regulatory relief and flexibility, and offer important business benefits throughout the product life cycle. QbD is increasingly becoming an important and widely used technique in the pharmaceutical industry. QbD can be considered to be system-based approach to the design, development, and delivery of any product or service to a consumer. It is an approach to pharmaceutical development that begins with predefined objectives and emphasizes product and process understanding and process control. Process parameters and quality attributes are identified for each unit operation. Benefits, opportunities, and steps involved in QbD of pharmaceutical products are described. The aim of pharmaceutical development is to design a quality product and its manufacturing process to consistently deliver the intended performance of the product. Quality cannot be tested into products, but quality should be built in by design. It includes the quality target product profile, critical quality attributes, and key aspects of QbD. It also gives comparison between product quality by end product testing and product quality by QbD. The foundation of QbD is ICH guidelines. Hence, if we identify the cause and effect relationship between the various inputs and responses by carefully designed experiments, we can control the quality of the product by simply controlling the inputs such as raw material specifications or process parameters.

This study investigates the influence of artificial intelligence (AI) integration on teachers’ professional identity and job satisfaction using a quantitative research design involving 251 respondents. Descriptive statistics showed relatively high levels of AI integration (M = 3.98, SD = 0.62) and professional identity (M = 4.12, SD = 0.58), indicating strong engagement with AI tools and a well-defined sense of professional role among teachers. Pearson correlation analysis revealed a moderately strong, statistically significant positive relationship between AI integration and professional identity (r = 0.612, p = 0.000), demonstrating that increased use of AI is associated with a strengthened professional identity. Mediation analysis further indicated that institutional factors significantly influence the relationship between AI integration and job satisfaction, with AI integration positively predicting institutional support (β = 0.54, p = 0.000) and institutional factors strongly predicting job satisfaction (β = 0.47, p = 0.000). Both a significant direct effect (β = 0.29, p = 0.001) and a strong indirect effect (β = 0.25, p = 0.000) were found, confirming partial mediation. These findings highlight that AI not only enhances teachers’ identity and satisfaction but that successful implementation relies heavily on institutional readiness and support. Overall, the results underscore the importance of adopting teacher-centered AI strategies that reinforce professional identity, reduce workload, and enhance well-being.

The pharmaceutical industry is undergoing a paradigm shift with the integration of artificial intelligence (AI) technologies into research and development workflows. One of the most promising applications lies in the optimization of pharmaceutical formulations and excipient selection—a traditionally empirical and time-consuming process. AI, particularly machine learning and neural networks, has demonstrated immense potential in modeling complex formulation behaviors, predicting excipient compatibility, and streamlining the path to robust product development. By analyzing large datasets and identifying hidden patterns, AI tools facilitate rapid screening of excipients, forecasting of stability profiles, and real-time decision-making in formulation design. Furthermore, AI-driven models can complement or even surpass traditional statistical methods such as design of experiments by enabling multi-objective optimization across various formulation parameters. Case studies involving tablets, lipid-based carriers, and nanotechnology-based systems illustrate the practical success of AI in enhancing formulation performance and reproducibility. Despite challenges such as data quality, algorithm transparency, and regulatory acceptance, the trajectory of AI adoption is accelerating, with its convergence with Quality by Design and process analytical technologies forming the foundation of next-generation pharmaceutical development. As AI technologies mature and become more interpretable and accessible, they are poised to redefine the role of formulation scientists and enable more personalized, efficient, and predictive formulation strategies. This manuscript explores the core principles, current applications, limitations, and future outlook of AI in pharmaceutical formulation science.

Quality by design (QbD) is an essential part of the modern advance to pharmaceutical quality. QbD is best key to build a quality in all pharmaceutical products. Under this concepts of be throughout design and growth of product, it is important to identify desire product performance report [Target product profile (TPP), Quality Target product profile (QTPP)] and identify critical quality attributes (CQA). To recognize the impact of raw material [critical material attributes (CAM)], critical process parameters (CPP) on the CQAs and identification and control sources of changeability. The plan of pharmaceutical development is to design a quality products and its manufacturing process always deliver the future performance of the product. The base of Quality by design is ICH Guidelines Q8 for Pharmaceutical for development, Q9 quality risk management, Q10 for pharmaceutical quality systems.

Introduction: The integration of artificial intelligence (AI) into higher education is swiftly revolutionizing pedagogical methodologies, in conjunction with learning processes and research paradigms. The interdisciplinary potential of AI within academic settings was examined in this study, employing a case study conducted at the University of Tirana. Through the utilization of bibliometric analysis and survey-based research, this study comprehensively investigates the swiftly emerging trends in AI applications, students' significant familiarity with AI technologies, as well as the substantial challenges impeding broader adoption. The bibliometric analysis highlights significant exponential growth in AI research, particularly within pivotal domains such as finance and accounting, thereby emphasizing the swiftly increasing relevance of blockchain and automation. The survey indicates a robust enthusiasm among students for AI in educational settings. More than 90 percent of students actively incorporate AI tools in their project work. Nonetheless, resource limitations and ethical considerations, including privacy, data security, and algorithmic bias, pose considerable challenges to the widespread adoption of AI, despite the prevailing enthusiasm. Objective: The aim of this research is to examine the incorporation of Artificial Intelligence (AI) within the realm of higher education. This analysis concentrates on the applications, advantages, and challenges associated with AI, with the intent of advancing interdisciplinary research and educational methodologies at the University of Tirana. Theoretical Framework: This research expands upon the principles of AI adoption within the educational sector, alongside an examination of ethical considerations and multidisciplinary collaboration. Theories pertaining to technological integration and adaptive learning systems serve as the foundational framework for comprehending the implications of AI in the realm of education. Method: The methodology employs bibliometric analysis to examine AI-related research trends utilizing data from SCOPUS and conducts a survey to assess students' familiarity with and perceptions of AI. Data collection was facilitated through bibliometric instruments and an online survey incorporating Likert-scale and open-ended questions. Results and Discussion: The results underscore an increased focus on artificial intelligence (AI) and blockchain within scholarly research, wherein students exhibit considerable engagement and interest in AI applications. Nevertheless, limitations in resources and ethical issues, including privacy and bias, persist as primary challenges. The discourse underscores the imperative for investment in infrastructure and the incorporation of ethical education. Research Implications: This research highlights the imperative for higher education institutions to integrate artificial intelligence tools, cultivate adaptive curricula, and address ethical considerations in order to adequately prepare students for a future shaped by AI. The implications of these findings also pertain to educational policy and the formulation of interdisciplinary research strategies. Originality/Value: The study contributes by delivering a comprehensive bibliometric analysis and gives insights into student engagement with artificial intelligence. Its significance resides in presenting actionable recommendations to enhance the integration of artificial intelligence in higher education.

Quality by design (QbD) principles have become widely adopted in the pharmaceutical industry to ensure consistent quality of products through their formulation, process development, and commercialization. Advanced analytical techniques, including spectroscopy, chromatography, microscopy, and thermal analysis, are vital in controlling the critical product and process parameters necessary to produce high-quality pharmaceutical products consistently. Manufacturers also employ advanced technologies such as continuous manufacturing to improve efficiency and lower costs. Quality risk management (QRM) principles are critical in identifying, evaluating, and controlling risks throughout the development process, including research, development, and manufacturing. Computer-aided process simulation has become more prevalent in optimizing pharmaceutical manufacturing processes. Understanding the science behind the product and the manufacturing process is crucial, as knowledge gained from early formulation and process development stages, including scale-up, is integral. Overall, pharmaceutical formulation and process development require a multidisciplinary approach incorporating advanced analytical techniques, manufacturing technologies, and QbD and QRM principles.

Microcrystalline cellulose (MCC) is an excellent excipient for the production of pellets by extrusion spheronization. However, it causes slow release rate of poorly water soluble drugs from pellets. Co-processed excipient prepared by spray drying (US4744987; US5686107; WO2003051338) and coprecipitation technique (WO9517831) are patented. The objective of present study was to develop co-processed MCC pellets (MOMLETS) by extrusion-spheronization technique using the principle of Quality by Design (QbD). Co-processed excipient core pellets (MOMLETS) were developed by extrusion spheronization technique using Quality by Design (QbD) approach. BCS class II drug (telmisartan) was layered onto it in a fluidized bed processor. Quality Target Product Profile (QTPP) and Critical Quality Attributes (CQA) for pellets were identified. Risk assessment was reported using Ishikawa diagram. Plackett Burman design was used to check the effect of seven independent variables; superdisintegrant, extruder speed, ethanol: water, spheronizer speed, extruder screen, pore former and MCC: lactose; on percentage drug release at 30 min. Pareto chart and normal probability plot was constructed to identify the significant factors. Box-Behnken design (BBD) using three most significant factors (Extruder screen size, type of superdisintegrant and type of pore former) was used as an optimization design. The control space was identified in which desired quality of the pellets can be obtained. Co-processed excipient core pellets (MOMLETS) were successfully developed by QbD approach. Versatility, Industrial scalability and simplicity are the main features of the proposed research.

To formulate and optimize Ozenoxacin nano-emulsion using Quality by Design (QbD) concept by means of Box-Behnken Design (BBD) and converting it to a gel to form Ozenoxacin nano-emulgel followed by physico-chemical, in-vitro, ex-vivo and in-vivo evaluation. This study demonstrates the application of QbD methodology for the development and optimization of an effective topical nanoemulgel formulation for the treatment of Impetigo focusing on the selection of appropriate excipients, optimization of formulation and process variables, and characterization of critical quality attributes. BBD was used to study the effect of "% of oil, % of Smix and homogenization speed" on critical quality attributes "globule size and % entrapment efficiency" for the optimisation of Ozenoxacin Nano-emulsion. Ozenoxacin loaded nano-emulgel was characterized for "description, identification, pH, specific gravity, amplitude sweep, viscosity, assay, organic impurities, antimicrobial effectiveness testing, in-vitro release testing, ex-vivo permeation testing, skin retention and in-vivo anti-bacterial activity". In-vitro release and ex-vivo permeation, skin retention and in-vivo anti-bacterial activity were found to be significantly (p < 0.01) higher for the nano-emulgel formulation compared to the innovator formulation (OZANEX™). Antimicrobial effectiveness testing was performed and found that even at 70% label claim of benzoic acid is effective to inhibit microbial growth in the drug product. The systematic application of QbD principles facilitated the successful development and optimization of a Ozenoxacin Nano-Emulsion. Optimised Ozenoxacin Nano-Emulgel can be considered as an effective alternative and found to be stable at least for 6months at 40°C / 75% RH and 30°C / 75% RH.

The purpose of this review is to discuss the challenges associated with the development of nanoparticle-based quality drug products in adhering to the principles of quality by design (QbD) and defining appropriate quality parameters towards successful product development. With the advent of nanotechnology into the pharmaceutical field, the novel field of nanomedicine was born. Due to their unique properties in terms of size, conformation and targeted delivery, nanomedicines are able to overcome many drawbacks of conventional medicine. As nano-sized formulations have made their way into more and more therapies, it has became clear that these very unique properties create hurdles for nanomedicines in successfully traversing the regulatory pathways and there is a need to develop nanomedicines in a more controlled and consistent fashion. The elements of a QbD methodology explained in this review enable the development of nano-based formulations in a way that maximizes the possibility of success. The identification of critical quality attributes (CQA) of the drug product and its intermediates are discussed in detail with a focus on nanomaterial-based formulations. In conclusion, QbD and the identification and specification of CQAs at its core are critical to the design, development and growth of nanomaterials in pharmaceuticals.

Natural killer (NK) cells are innate lymphocytes capable of directly targeting and killing tumor cells without prior sensitization 1 . Due to their inherent cytotoxicity, NK cells have emerged as a very promising modality for cancer immunotherapy, offering outstanding advantages over chimeric antigen receptor-T (CAR-T) cell therapies, such as reduced risks of the cytokine release syndrome and neurotoxicity [1][2][3] . CAR-engineered NK cells further enhance therapeutic potential by improving target specificity and cytotoxic efficacy, thereby complementing and extending the clinical success of current CAR-T cell therapies 1,4 . However, the lack of formally defined critical quality attributes (CQAs) currently limits cross-trial and cross-study comparability, compromises safety evaluation, and impedes clinical efficacy optimization for NK cell-based therapies.CQAs play a pivotal role in biopharmaceutical development as they describe the required product quality, safety and efficacy 5 . According to the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) Guideline Q8(R2), a CQA is a "physical, chemical, biological, or microbiological property or characteristic that should be within an appropriate limit, range, or distribution to ensure the desired product quality" 6 . Despite their central importance, CQAs have not yet been formally defined for NK cell therapies yet. The current FDA guidance only roughly defines parameters for quality testing, recommending that CQAs should minimally consist of identity, quality, purity, and potency 7,8 . Regulatory agencies currently refrain from defining generalized acceptance criteria or fixed limits, as these must be adapted to each manufacturing platform, therapeutic indication, and target patient group. While clearly defined CQAs with specified ranges would bring obvious benefits, overly rigid or premature standardization could stifle innovation, particularly for next-generation therapies tailored to specific indications or specific patient populations. In contrast, the current high variance in trial release criteria is complicating product comparison and poses challenges for a consistent regulatory evaluation. We are convinced that a harmonized effort for defining CQAs -anchored in a Quality by Design (QbD) framework and aligned with ICH and regulatory guidelines -is required and essential for ensuring the consistency, safety, and efficacy of NK cell therapies.To potentially initiate this harmonization effort, we systematically analyzed the reported release criteria from all completed Phase 1 and 2 clinical trials of NK cell therapies targeting cancer, which were conducted in the US and Europe with reported release criteria 9-14 (Table 1).In all the clinical trials the accepted range for NK cells was uniformly set at 46 highlighting its importance as a benchmark linked to clinical performance. Purity was defined 47 by the of other cell types in the final NK cell cell were 48 identified by surface NK cells (CD56+) , T cells (CD3+), B cells (CD19+, 49 CD20+), (CD14+), and NK-like T cells (CD16+CD3+). Activation (NKG2D, 50 NKp44, NKp46) and inhibitory receptors (NKG2A) were reported a variable extent, 51 and NK cell purity criteria of the final product ranged from ≥30% to ≥90% 9-12, 14 . T and B cell 52 contaminants were strictly limited between ≤0.2% to <3%

Diabetes mellitus is a chronic metabolic disorder with significant global prevalence and associated healthcare burdens, necessitating early detection and effective management strategies. The integration of Machine Learning (ML) and Artificial Intelligence (AI) has revolutionized diabetes care, offering innovative approaches to prediction, monitoring, and personalized management. This study conducted a systematic review of 82 high-quality peer-reviewed articles, following the PRISMA guidelines, to provide a comprehensive evaluation of ML and AI applications in diabetes prediction and management. The review highlights the widespread adoption of supervised learning models, such as Random Forest and Support Vector Machines (SVM), which consistently demonstrate high accuracy and reliability in predicting diabetes risk. Ensemble learning methods, particularly Gradient Boosting, emerged as superior techniques for predictive performance, while deep learning models, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), proved effective in analyzing unstructured data such as medical images and time-series glucose data. The integration of AI into wearable devices and mobile health applications has further enhanced real-time monitoring and glycemic control, bridging the gap between technological advancements and practical healthcare solutions. Despite these advancements, challenges such as data imbalance, limited external validation, and the need for explainable AI frameworks persist, underscoring the necessity for methodological rigor and standardization. This review provides critical insights into the current state, limitations, and opportunities of ML and AI in diabetes care, emphasizing their transformative potential in addressing this global health challenge.