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    Developing alfuzosin tablet formulation based on quality by design (QbD) approach by using artificial neural network
    (Colegio Farmaceuticos Provincia De Buenos Aires, 2019) Aksu, Buket; Mesut, Burcu; Özsoy, Yıldız
    The purpose of this study was to develop sustained release direct compressible alfuzosin (ALF) hydrochloride (HCl) tablet based on the concept of quality by design (QbD) approach using artificial neural network programs. At the first step of the study, the target product profile (TPP) of the formulation was defined. Subsequently, risk assessment tools were used to determine critical quality attributes (CQAs) and critical formulation parameters (CFPs). In-process control tests, assay and dissolution studies were performed. The test results were transferred to the artificial neural network (ANN) and the program was trained based on these data. The program offered new tablet formulations which have not been studied before and dissolution test results of this formulation was highly similar to the reference product's results than the other formulations. In conclusion, using the ANN programs within the scope of QbD approach for solid dosage formulation developments brings a lot of industry-wide benefits and advantages to ease scaling-up and meet the recent ICH guideline requirements.
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    Examining the basic principles of quality by design (QbD) approach in analytical studies
    (2021) Mesut, Burcu; Önal, Cem; Aksu, Buket; Özsoy, Yıldız
    Although the first application of Quality by Design (QbD) concept started for product development studies, the number of studies regarding its application to analytical development has been increased recently. Basically, QbD strategy in both formulation development and analytic studies are identical logically and conceptually, but they have somedifferences in terms of its terminology and application. Essential terminology and approach differences in this concept are; the determination of the analytic target profile, critical method characteristics, critical process parameters, and the determination of the method study area. However, the risk evaluation method which is necessary for the appropriate application of quality by design is also an inseparable part of the analytical quality by design. Despite those terminological differences, developing a quality based method with the analytical design that contributes to research with an appropriately applied risk-based design quality approach and provides multiple advantages that will be noticed each and every time, will be useful both for researchers and authorities who investigate license documentation and changes. Therefore, the terminology which is used for analytic quality by design and appropriate risk evaluation approaches are explained in this study.
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    Quality by design (QbD) approach and application of preformulation studies for a poorly water soluble model drug, Nimesulid
    (2023) Özalp, Yıldız; Khamis, Hala; Jiwa, Nailla; Mesut, Burcu; Aksu, Buket
    Formulating poorly water-soluble medications is one of the most crucial challenges encountered in the pharmaceutical industry. Due to this obstacle, the model drug chosen for this research was Nimesulide. The primary goal of this research was to obtain information on the drug's physical and chemical properties, either alone or in combination with excipients, in order design a formulation and create a stable and bioavailable dosage form. Formulations were designed using Flowlac (R) 100 and Avicel (R) 102 as fillers, Kollidon (R) 30 as a binder, Magnesium stearate as a lubricant, and variable concentrations of Kollidon (R) CL and Primojel as superdisintegrants. The tableting process was conducted using a compaction simulator. The Quality by Design (QbD) approach allows formulators to enhance product development with built-in product quality. In this study, to understand the relationship between excipients and compaction force differences on tablet properties, the QbD approach was applied by using a compaction simulator.
  • Küçük Resim
    Öğe
    Quality by design (QbD) for pharmaceutical area
    (Istanbul Univ, Fac Pharmacy, 2015) Aksu, Buket; Mesut, Burcu
    Recent changes and limited resources for drug development and manufacturing have rendered the conventional pharmaceutical quality assurance approach insufficient and have given rise to new research in these areas. To address these research efforts, the FDA improved and modernized the rules governing pharmaceutical manufacturing and product quality in 2002, thereby realizing a paradigm change in the current Good Manufacturing Practices (cGMP). The Quality by Design (QbD) approach has entered the pharmaceutical industry within the last 10 years after the approval of the ICH Q8 in 2005. QbD is based on an understanding of the target product's quality profile (QTPP) and an assessment of its risks during the design and development of pharmaceutical dosage forms. By determining the critical quality attributes of the drug, including its active ingredient, its excipients, and the processes and design spaces used during the R&D phase, multi-way tracking during the life cycle of the drug can be achieved. This tracking can provide numerous advantages, including flexibility in licensing by decreasing the variation and type modifications in applications of the pharmaceutical product, which result from minimizing the possible issues arising after the release of the product. When all these data are observed, it is clear that the new QbD approach benefits the authorities, the drug manufacturers and the patient. Although QbD has certain challenges during its early stages, it is thought that QbD will benefit pharmaceutical manufacturers.
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    Quetiapine Fumarate Extended-release Tablet Formulation Design Using Artificial Neural Networks
    (Turkish Pharmacists Assoc, 2017) Ozcelik, Esher; Mesut, Burcu; Aksu, Buket; Ozsoy, Yildiz
    Objectives: This design study was implemented within the scope of the quality by design approach, which included the International Conference on Harmonization guidelines. We evaluated the quality of a modified-release tablet formulation of quetiapine fumarate, which was designed using artificial neural networks (ANN), and determined a new formulation that was similar to the reference product. Materials and Methods: Twelve different formulations were produced and tested. The reference product's results and our experimental results were used as outputs for the training of the ANN programs of Intelligensys Ltd. Results: Dissolution tests were performed with the new formulation (F13) suggested by the INForm V.4 ANN program in three different pHs of the gastrointestinal system. The compliance of this formulation was confirmed by comparing the results with an f2 similarity test. Conclusion: Use of these programs supports research and development processes with multiple evaluation methods and alternative formulations may be determined faster and at lower cost.
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    Role of artificial intelligence in quality profiling and optimization of drug products
    (Elsevier, 2023) Mesut, Burcu; Başkor, Atakan; Aksu, Neşe Buket
    In the pharmaceutical industry, one of the sectors with the highest research and development potential today, the development of safe and effective new drug active ingredients and formulations is an expensive and long process. It has rapidly adapted to the developing technologies; modern production tools have been developed to help increase the production quality as a result of the important developments in quality and risk management systems in recent years. The development of these modern tools has resulted in positive outcomes such as increase in quality and decrease in cost of the products, advocating quality while designing instead of testing it as stated in the ICH Q8 guide. With these approaches, it is attempted to reduce the uncertainty and pharmaceutical authorities have published the ICH Q9 guide, which will meet this need and be used to identify risks and made it available to the pharmaceutical industry. This guide defines risk and describes the methodologies that can be applied to identify risks in processes and drug design. The ICH Q8 and ICH Q9 guidelines should be applied together and recommend using statistical tools in the formulation development process. During the formulation development phase, both the design areas and the determination of the most appropriate formulation create very complex data and models while trying to analyze studies in theory and practice, and although there is sufficient knowledge, the human capacity to evaluate the whole data at once is limited. Therefore, it is necessary to get support from various software in order to manage the drug development process more efficiently. Machine learning methods can quickly help create and improve products, increase productivity, consistency, and quality. Machine learning models make predictions based on empirical data and provide an excellent opportunity to develop the most appropriate and effective formulations.